{
 "cells": [
  {
   "cell_type": "raw",
   "id": "85f90af6-9c2d-4613-ae56-f94276c0285b",
   "metadata": {},
   "source": [
    "测试SI方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b90e8bc5-a851-4915-a0b2-0d80d8814714",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 155c1042 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete ✅ (128 CPUs, 1007.5 GB RAM, 18.4/30.0 GB disk)\n"
     ]
    }
   ],
   "source": [
    "import comet_ml\n",
    "import torch\n",
    "import utils\n",
    "\n",
    "comet_ml.init(project_name='exp_100epoch')\n",
    "# 这里应该会包含100epoch的0,0.6,1.2加雾以及各个以100epoch为单位的增量\n",
    "display = utils.notebook_init()  # checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "108aeb3e-962d-4d1e-aaba-f0cd8bc78e38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "from fog_test.for_different_strength import mix_dataset\n",
    "origin_ratio = {\n",
    "    '1.0':1,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )\n",
    "\n",
    "val_fogged_strength = 1.0\n",
    "# 替换验证集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/val/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "echo 'Val set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d169343c-2daf-4ae7-8d2b-26036df46f88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# # 替换验证集, 为了测灾难性遗忘所以用无雾验证集\n",
    "# update_testsets = f\" \\\n",
    "# rm ../datasets/kitti/images/val/* &&\\\n",
    "# cp /root/autodl-tmp/datasets/kitti/images/origin_val/* ../datasets/kitti/images/val/ && \\\n",
    "# echo 'Test set updated successfully!' \\\n",
    "# \" \n",
    "# !{update_testsets}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57815b5a-5174-40cb-b117-53ec666b64f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0_to_fog_1.0_plain, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/b5abb95b116f4ae0b6ba5b76a30a7928\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val... 1048 images, 0 backg\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/val.cache\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0_to_fog_1.0_plain/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0_to_fog_1.0_plain\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.65G    0.03486    0.03428   0.006893        128        640: 1\n",
      "tensor([0.87622], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.789      0.515      0.585      0.365\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.65G     0.0337    0.03052   0.005239        133        640: 1\n",
      "tensor([0.93715], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.445      0.315      0.296      0.171\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.65G     0.0365    0.03302   0.006499        131        640: 1\n",
      "tensor([1.01537], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.636     0.0711     0.0905     0.0498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.65G    0.03839    0.03477   0.007357        108        640: 1\n",
      "tensor([0.88693], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.445      0.145       0.15     0.0828\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.65G    0.03825    0.03359   0.006356        156        640: 1\n",
      "tensor([0.97841], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.772      0.498        0.6      0.348\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.65G    0.03721    0.03247   0.005778        123        640: 1\n",
      "tensor([0.90661], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.732      0.488      0.569      0.311\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.65G    0.03643    0.03131   0.005096        174        640: 1\n",
      "tensor([1.05071], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.589      0.679      0.393\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.65G    0.03587    0.03066   0.005019        166        640: 1\n",
      "tensor([1.10978], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.691      0.793      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.65G    0.03511    0.03063   0.004694        152        640: 1\n",
      "tensor([0.92849], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.674      0.755       0.44\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.65G    0.03493     0.0305   0.004777        136        640: 1\n",
      "tensor([0.88823], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.824      0.669      0.762      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.65G    0.03465    0.03022   0.004462        134        640: 1\n",
      "tensor([0.86391], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.694      0.777      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.65G    0.03424    0.02959   0.004339        182        640: 1\n",
      "tensor([0.91177], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.825      0.577      0.665      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.65G    0.03387    0.02989   0.004222        128        640: 1\n",
      "tensor([0.79485], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865       0.74       0.81      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.65G    0.03357    0.02902   0.004022        112        640: 1\n",
      "tensor([0.84985], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.724       0.81      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.65G    0.03321    0.02921   0.004097        151        640: 1\n",
      "tensor([0.83255], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.707       0.82      0.523\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.65G    0.03275    0.02894   0.003886        132        640: 1\n",
      "tensor([0.79535], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.763      0.836      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.65G    0.03288    0.02868    0.00387        131        640: 1\n",
      "tensor([0.78754], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87       0.75      0.828       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.65G     0.0328    0.02859   0.003908        159        640: 1\n",
      "tensor([0.92287], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.761      0.828      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.65G    0.03242    0.02832   0.003796        125        640: 1\n",
      "tensor([0.77918], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.683      0.797      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.65G    0.03251    0.02867   0.003817         88        640: 1\n",
      "tensor([0.70948], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.852      0.717      0.813      0.516\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.65G    0.03198    0.02777   0.003808        137        640: 1\n",
      "tensor([0.92322], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.761      0.843      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.65G    0.03154    0.02809   0.003562        166        640: 1\n",
      "tensor([0.94037], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896       0.74      0.833      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.65G    0.03166    0.02783   0.003508        161        640: 1\n",
      "tensor([0.87835], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.763      0.842       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.65G    0.03164    0.02746   0.003572        118        640: 1\n",
      "tensor([0.77680], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.759      0.833      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.65G    0.03114    0.02747   0.003458        151        640: 1\n",
      "tensor([0.85118], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.716      0.823      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.65G    0.03118    0.02768   0.003428        133        640: 1\n",
      "tensor([0.80940], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908       0.78      0.853      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.65G    0.03106    0.02728   0.003335        154        640: 1\n",
      "tensor([0.92414], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.795      0.856      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.65G    0.03083     0.0274   0.003414        122        640: 1\n",
      "tensor([0.75125], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.701      0.809      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.65G    0.03073    0.02722   0.003296        123        640: 1\n",
      "tensor([0.66622], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909       0.78      0.865      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.65G    0.03047    0.02678   0.003219        127        640: 1\n",
      "tensor([0.68724], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.771      0.861      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.65G    0.03002    0.02588   0.003163        127        640: 1\n",
      "tensor([0.68269], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.798      0.867       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.65G     0.0303    0.02651   0.003182        122        640: 1\n",
      "tensor([0.77243], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.799      0.861      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.65G    0.03011    0.02669   0.003183        146        640: 1\n",
      "tensor([0.82794], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.802      0.873      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.65G    0.03007    0.02616   0.003092        202        640: 1\n",
      "tensor([0.87670], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.791      0.865      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.65G    0.02968    0.02595   0.003118         94        640: 1\n",
      "tensor([0.62945], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.795      0.865      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.65G    0.02968    0.02596    0.00302        152        640: 1\n",
      "tensor([0.83553], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.797      0.864      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.65G    0.02917    0.02582   0.003087        123        640: 1\n",
      "tensor([0.67382], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.806      0.863      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.65G    0.02945    0.02605   0.003076        162        640: 1\n",
      "tensor([0.72866], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.796      0.874      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.65G    0.02957    0.02628   0.003044        161        640: 1\n",
      "tensor([0.79832], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.784      0.866       0.58\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.65G     0.0293    0.02602   0.002992        122        640: 1\n",
      "tensor([0.64650], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.807      0.874      0.593\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.65G    0.02895    0.02554   0.002996        126        640: 1\n",
      "tensor([0.66549], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.826       0.88      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.65G    0.02892    0.02566   0.002955         90        640: 1\n",
      "tensor([0.64009], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.826      0.888      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.65G    0.02871     0.0254    0.00283        118        640: 1\n",
      "tensor([0.74788], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.799      0.877        0.6\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.65G    0.02865    0.02546   0.002743        157        640: 1\n",
      "tensor([0.78027], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.806      0.879      0.603\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.65G    0.02857    0.02538   0.002809        104        640: 1\n",
      "tensor([0.55340], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.801      0.874      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.65G    0.02857     0.0256   0.002701        157        640: 1\n",
      "tensor([0.72890], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.806      0.884      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.65G    0.02849      0.025   0.002675        108        640: 1\n",
      "tensor([0.55842], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.818      0.882      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.65G    0.02826    0.02497   0.002764        159        640: 1\n",
      "tensor([0.74012], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.806       0.88      0.608\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.65G    0.02786    0.02486   0.002695        118        640: 1\n",
      "tensor([0.68642], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.791      0.874      0.609\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.65G    0.02816    0.02525   0.002676        176        640: 1\n",
      "tensor([0.88054], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.813      0.883      0.606\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.65G    0.02816    0.02488   0.002716        130        640: 1\n",
      "tensor([0.70387], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.796       0.87      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.65G    0.02767    0.02492   0.002628        178        640: 1\n",
      "tensor([0.87635], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.816      0.875      0.603\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.65G    0.02762    0.02459    0.00262        148        640: 1\n",
      "tensor([0.70460], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.815      0.882       0.61\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.65G    0.02752    0.02446    0.00256        115        640: 1\n",
      "tensor([0.64466], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.819      0.876       0.61\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.65G    0.02747    0.02428   0.002555        124        640: 1\n",
      "tensor([0.64266], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.814      0.888      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.65G    0.02704    0.02393   0.002526        163        640: 1\n",
      "tensor([0.67814], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.817      0.885      0.613\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.65G    0.02712    0.02436   0.002569        200        640: 1\n",
      "tensor([0.79404], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.823      0.886      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.65G    0.02707    0.02429   0.002572        141        640: 1\n",
      "tensor([0.69233], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.838      0.894      0.621\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.65G    0.02706    0.02422   0.002597        146        640: 1\n",
      "tensor([0.68706], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.823      0.892      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.65G    0.02692    0.02408   0.002504        168        640: 1\n",
      "tensor([0.71621], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.837      0.894      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.65G    0.02684    0.02381   0.002439        175        640: 1\n",
      "tensor([0.74557], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915       0.83      0.893      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.65G    0.02656    0.02392   0.002472        139        640: 1\n",
      "tensor([0.75424], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.814      0.889      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.65G    0.02643    0.02324    0.00245        117        640: 1\n",
      "tensor([0.64847], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.827      0.887      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.65G    0.02658    0.02355   0.002372        129        640: 1\n",
      "tensor([0.66344], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.818      0.881      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.65G    0.02635    0.02358   0.002457        109        640: 1\n",
      "tensor([0.60407], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.839      0.891       0.63\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.65G    0.02636    0.02369   0.002343        154        640: 1\n",
      "tensor([0.75100], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.829      0.894      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.65G    0.02609    0.02346   0.002383        119        640: 1\n",
      "tensor([0.64285], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.813      0.887      0.634\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.65G    0.02604    0.02296   0.002375        153        640: 1\n",
      "tensor([0.70375], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.829      0.887      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.65G    0.02602    0.02338   0.002384        116        640: 1\n",
      "tensor([0.60201], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.937      0.823      0.891      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.65G    0.02561    0.02274   0.002376        141        640: 1\n",
      "tensor([0.70694], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.822      0.886      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.65G    0.02575    0.02302   0.002281        175        640: 1\n",
      "tensor([0.79872], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.824      0.888      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.65G    0.02589    0.02302   0.002344        161        640: 1\n",
      "tensor([0.70766], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.822      0.887       0.63\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.65G    0.02558    0.02267   0.002213        114        640: 1\n",
      "tensor([0.60760], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.836       0.89      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.65G    0.02573    0.02313     0.0023        141        640: 1\n",
      "tensor([0.70491], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.829      0.887      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.65G    0.02556    0.02301   0.002262        133        640: 1\n",
      "tensor([0.59256], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.835      0.895      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.65G    0.02525    0.02249   0.002176        159        640: 1\n",
      "tensor([0.74001], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.833      0.891      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.65G    0.02546    0.02251   0.002266        122        640: 1\n",
      "tensor([0.55616], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.847      0.895      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.65G    0.02542     0.0228   0.002215        137        640: 1\n",
      "tensor([0.65278], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.843       0.89      0.634\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.65G    0.02531    0.02232   0.002206        137        640: 1\n",
      "tensor([0.64524], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.835      0.893      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.65G    0.02476    0.02191   0.002217        161        640: 1\n",
      "tensor([0.73391], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913       0.84      0.891      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.65G    0.02496    0.02239   0.002142        154        640: 1\n",
      "tensor([0.62644], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.833      0.897      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.65G    0.02485    0.02203   0.002145        181        640: 1\n",
      "tensor([0.73247], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.846       0.89      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.65G    0.02475    0.02202   0.002141        149        640: 1\n",
      "tensor([0.61739], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.839      0.895      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.65G    0.02464    0.02205   0.002072        118        640: 1\n",
      "tensor([0.59445], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.842      0.892      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.65G    0.02451    0.02189   0.002101        178        640: 1\n",
      "tensor([0.73559], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.94      0.831      0.892      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.65G    0.02442    0.02181    0.00205        140        640: 1\n",
      "tensor([0.65241], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.846      0.894      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.65G     0.0245     0.0219   0.002119        119        640: 1\n",
      "tensor([0.52392], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.832      0.894      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.65G    0.02436    0.02198   0.002044        114        640: 1\n",
      "tensor([0.49844], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911       0.85      0.894      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.65G    0.02447    0.02166   0.002117        117        640: 1\n",
      "tensor([0.54517], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.937      0.826      0.895      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.65G    0.02435    0.02178   0.002015        118        640: 1\n",
      "tensor([0.55598], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.935       0.82      0.892      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.65G    0.02419    0.02148   0.001978        115        640: 1\n",
      "tensor([0.57990], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.844      0.901      0.658\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.65G    0.02392    0.02119   0.001969        159        640: 1\n",
      "tensor([0.73937], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.844      0.899      0.657\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.65G    0.02402    0.02147   0.002026        165        640: 1\n",
      "tensor([0.68237], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.857      0.899      0.654\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.65G    0.02389    0.02113   0.002024        126        640: 1\n",
      "tensor([0.57596], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.848      0.897      0.658\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.65G    0.02379    0.02113    0.00203        112        640: 1\n",
      "tensor([0.56526], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.838      0.897      0.654\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.65G    0.02358    0.02097   0.001882        121        640: 1\n",
      "tensor([0.58768], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.849      0.897      0.654\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.65G    0.02386    0.02099   0.002002        195        640: 1\n",
      "tensor([0.63227], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.935      0.835      0.895      0.655\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.65G    0.02383    0.02104   0.001939        101        640: 1\n",
      "tensor([0.58344], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.856      0.897      0.657\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.65G    0.02352     0.0209   0.002009        137        640: 1\n",
      "tensor([0.57458], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.855      0.897      0.655\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.65G    0.02361    0.02104   0.001902        115        640: 1\n",
      "tensor([0.49200], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.849      0.897      0.661\n",
      "\n",
      "100 epochs completed in 0.980 hours.\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_plain/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_plain/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0_to_fog_1.0_plain/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924       0.85      0.896       0.66\n",
      "                   Car       1048       4012       0.94      0.923      0.968      0.792\n",
      "                   Van       1048        431      0.937       0.94      0.969      0.793\n",
      "                 Truck       1048        166      0.934      0.937      0.967      0.778\n",
      "                  Tram       1048         56      0.942      0.946       0.95      0.729\n",
      "            Pedestrian       1048        618      0.864       0.72      0.817      0.462\n",
      "        Person_sitting       1048         20      0.991       0.75      0.754      0.501\n",
      "               Cyclist       1048        234      0.904      0.806      0.867      0.587\n",
      "                  Misc       1048        138      0.877      0.779      0.879      0.638\n",
      "Results saved to \u001b[1mruns/train/fog_0_to_fog_1.0_plain\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m The process of logging environment details (conda environment, git patch) is underway. Please be patient as this may take some time.\n",
      "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Failed to complete logging of all environment details (conda environment, git patch)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0_to_fog_1.0_plain\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/b5abb95b116f4ae0b6ba5b76a30a7928\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.931770877558591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 234.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9679751514680017\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7917912302868225\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.940470934681529\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9232303090727817\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3704.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8520828619713524\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 20.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8665965264182043\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5872847306151469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.9040949332593391\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.8057296877457244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 189.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8251023436582182\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 15.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8786406601746981\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.638222338088309\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8774974161153023\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.7786117034909306\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 107.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7852880711832985\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 70.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.817156978109842\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.4623696722158397\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8635041361014116\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7200647249190939\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 445.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.8539182960271958\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.7537916545928718\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.5012856927695925\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9912658384880607\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.75\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 15.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9444392334582007\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9501289875173368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7291570203624178\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.9424582408923634\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9464285714285714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9355383387608063\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9667907512445731\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7779378125432564\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9339581627656082\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9371238708588107\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 156.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9385508769248324\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 27.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9688426191780908\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7928004247990096\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9374958210133445\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9396083102274334\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 405.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5218462944030762, 2.199005603790283)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.09053599007440205, 0.9008031021328169)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.04978779031648964, 0.6608966182919636)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.4446607070940116, 0.9402838545212171)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.07113747912002713, 0.8570762825496603)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.023523645475506783, 0.0383877195417881)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0018821274861693382, 0.007357478141784668)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.020904000848531723, 0.03477301076054573)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.024998458102345467, 0.07215876132249832)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0028458773158490658, 0.03572942316532135)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03949633613228798, 0.16256806254386902)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0_to_fog_1.0_plain\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/b5abb95b116f4ae0b6ba5b76a30a7928\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0_to_fog_1.0_plain\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.80 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 208 metrics, params and output messages\n",
      "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Failed to log run in comet.com\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name fog_0_to_fog_1.0_plain \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0bbb319-9861-4fe3-aefa-f99a9520afab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_plain/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.845      0.623      0.732      0.454\n",
      "                   Car       2244       8711      0.914      0.763      0.879       0.63\n",
      "                   Van       2244        861      0.824      0.635      0.752      0.512\n",
      "                 Truck       2244        333       0.96      0.733      0.824      0.609\n",
      "                  Tram       2244        138      0.953      0.591      0.793      0.447\n",
      "            Pedestrian       2244       1286      0.739      0.617      0.685      0.358\n",
      "        Person_sitting       2244         89      0.674      0.596       0.66      0.328\n",
      "               Cyclist       2244        496      0.878      0.463      0.584      0.329\n",
      "                  Misc       2244        284      0.815      0.589      0.679      0.418\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp66\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_plain/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10f63580-42df-49d5-a2ed-9abe0e9a31e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08c56f1d-ffa9-45bb-ac69-676bc0d53128",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "995d89af-36fa-4b68-86fc-e89f152747f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0_to_fog_1.0_SI_0.005, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=./runs/train/fog_02/weights/si.pt, SI_lambda=0.005\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/e252c729602a420e8610ece3ebbe5389\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val... 1048 images, 0 backg\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/val.cache\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0_to_fog_1.0_SI_0.005/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0_to_fog_1.0_SI_0.005\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.61G    0.04172    0.04829    0.01222        181        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/99      3.61G    0.03491    0.03427   0.006935        128        640: 1\n",
      "tensor([0.88930], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00010], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.662       0.39      0.433      0.261\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.61G    0.03312    0.03018   0.005119        185        640:  \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "       1/99      3.61G    0.03379     0.0307   0.005475        133        640: 1\n",
      "tensor([0.96269], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00015], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.514      0.222      0.237      0.131\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.61G     0.0363    0.03286    0.00623        131        640: 1\n",
      "tensor([0.97788], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00021], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.454      0.215      0.219      0.116\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.61G    0.03828    0.03438   0.007073        108        640: 1\n",
      "tensor([0.84067], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00044], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.505      0.262      0.253      0.134\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.61G    0.03831    0.03367   0.006651        156        640: 1\n",
      "tensor([1.05089], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00075], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.6      0.418       0.43      0.222\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.61G    0.03748    0.03241   0.005797        123        640: 1\n",
      "tensor([0.87428], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00092], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.807      0.644      0.725      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.61G    0.03637    0.03137   0.005124        174        640: 1\n",
      "tensor([1.01861], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00103], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.795      0.646      0.727      0.428\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.61G    0.03571    0.03051   0.004996        166        640: 1\n",
      "tensor([1.05442], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00111], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.817      0.697      0.769      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.61G    0.03523    0.03064   0.004672        152        640: 1\n",
      "tensor([0.92666], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00118], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.861       0.66      0.769      0.467\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.61G    0.03477    0.03052   0.004779        136        640: 1\n",
      "tensor([0.90417], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00125], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.633       0.73      0.428\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.61G    0.03479    0.03022    0.00456        134        640: 1\n",
      "tensor([0.86765], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00133], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.8      0.682      0.757      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.61G    0.03388    0.02937    0.00435        182        640: 1\n",
      "tensor([0.95272], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00138], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.832      0.692      0.786      0.485\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.61G    0.03407    0.02987   0.004326        128        640: 1\n",
      "tensor([0.76691], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00143], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.731      0.822      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.61G    0.03344    0.02901   0.004043        112        640: 1\n",
      "tensor([0.91968], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00148], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.728      0.808      0.493\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.61G    0.03325    0.02909   0.004034        151        640: 1\n",
      "tensor([0.86243], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00151], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.734      0.829      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.61G    0.03291    0.02893   0.003833        132        640: 1\n",
      "tensor([0.83985], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00154], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.769      0.845      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.61G    0.03246    0.02852    0.00386        131        640: 1\n",
      "tensor([0.82723], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.728      0.817        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.61G    0.03292    0.02856   0.003848        159        640: 1\n",
      "tensor([0.98781], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00159], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.721      0.844      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.61G    0.03245    0.02833   0.003835        125        640: 1\n",
      "tensor([0.73740], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00162], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.813      0.707      0.784      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.61G    0.03231    0.02855   0.003735         88        640: 1\n",
      "tensor([0.74671], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00167], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.765      0.849      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.61G    0.03193    0.02768    0.00365        137        640: 1\n",
      "tensor([0.90563], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00169], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.753      0.851      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.61G    0.03176    0.02828   0.003647        166        640: 1\n",
      "tensor([0.91971], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00170], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.724      0.818      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.61G    0.03162    0.02784   0.003514        161        640: 1\n",
      "tensor([0.88762], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00173], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.742      0.838      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.61G     0.0315    0.02736   0.003527        118        640: 1\n",
      "tensor([0.75259], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00174], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.752      0.847      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.61G    0.03125     0.0274   0.003451        151        640: 1\n",
      "tensor([0.87590], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00175], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.746      0.848      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.61G    0.03093    0.02755   0.003394        133        640: 1\n",
      "tensor([0.76226], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00176], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93       0.76      0.852      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.61G    0.03103    0.02723   0.003325        154        640: 1\n",
      "tensor([0.89836], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.764      0.848      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.61G    0.03091    0.02741   0.003451        122        640: 1\n",
      "tensor([0.75669], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.773      0.839      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.61G    0.03068    0.02719    0.00328        123        640: 1\n",
      "tensor([0.64887], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.782      0.853      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.61G    0.03044    0.02673   0.003205        127        640: 1\n",
      "tensor([0.69245], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.752      0.833      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.61G    0.03023    0.02593   0.003136        127        640: 1\n",
      "tensor([0.68877], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.744      0.842      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.61G    0.03029    0.02651   0.003201        122        640: 1\n",
      "tensor([0.77791], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00182], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.771      0.859      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.61G    0.03014    0.02673   0.003122        146        640: 1\n",
      "tensor([0.84955], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.794      0.858      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.61G    0.02971    0.02611   0.003126        202        640: 1\n",
      "tensor([0.93866], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.762      0.841      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.61G    0.02981    0.02605   0.003211         94        640: 1\n",
      "tensor([0.65740], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.798      0.859      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.61G     0.0296    0.02606    0.00309        152        640: 1\n",
      "tensor([0.80939], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.807      0.868      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.61G    0.02951    0.02587   0.003064        123        640: 1\n",
      "tensor([0.68827], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.773      0.863      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.61G    0.02946    0.02603   0.003096        162        640: 1\n",
      "tensor([0.75842], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.789      0.856      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.61G    0.02944    0.02621   0.003077        161        640: 1\n",
      "tensor([0.78806], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872       0.81      0.877      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.61G    0.02929    0.02596   0.002997        122        640: 1\n",
      "tensor([0.68886], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908       0.78       0.87      0.583\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.61G    0.02901    0.02558   0.003015        126        640: 1\n",
      "tensor([0.66520], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.796      0.872      0.583\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.61G    0.02894    0.02575   0.003044         90        640: 1\n",
      "tensor([0.62404], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.855      0.545      0.645      0.411\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.61G    0.02878    0.02554   0.002823        118        640: 1\n",
      "tensor([0.74903], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.777      0.872      0.588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.61G    0.02888     0.0256   0.002744        157        640: 1\n",
      "tensor([0.77269], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.816      0.877      0.593\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.61G    0.02875    0.02546   0.002843        104        640: 1\n",
      "tensor([0.57384], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.804      0.878      0.594\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.61G    0.02852    0.02564   0.002696        157        640: 1\n",
      "tensor([0.75924], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.819      0.884      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.61G    0.02841      0.025   0.002686        108        640: 1\n",
      "tensor([0.55889], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903        0.8      0.868      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.61G    0.02843    0.02507   0.002742        159        640: 1\n",
      "tensor([0.76517], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.832       0.88        0.6\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.61G    0.02807    0.02494   0.002772        118        640: 1\n",
      "tensor([0.68532], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.813      0.878      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.61G    0.02808    0.02525   0.002697        176        640: 1\n",
      "tensor([0.89049], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.812      0.878      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.61G    0.02803    0.02486   0.002723        130        640: 1\n",
      "tensor([0.69146], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.834      0.883      0.608\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.61G    0.02776    0.02495   0.002615        178        640: 1\n",
      "tensor([0.87499], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.817      0.877      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.61G     0.0276    0.02456   0.002623        148        640: 1\n",
      "tensor([0.70590], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.805      0.883      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.61G    0.02754    0.02443   0.002574        115        640: 1\n",
      "tensor([0.65021], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907       0.82      0.884      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.61G    0.02743    0.02431   0.002588        124        640: 1\n",
      "tensor([0.66224], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.803      0.872      0.605\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.61G    0.02712    0.02401   0.002578        163        640: 1\n",
      "tensor([0.71523], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.819      0.881      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.61G     0.0272    0.02433   0.002535        200        640: 1\n",
      "tensor([0.78827], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.803      0.882      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.61G    0.02709    0.02426   0.002525        141        640: 1\n",
      "tensor([0.67134], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00188], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.818      0.889      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.61G    0.02713     0.0242    0.00258        146        640: 1\n",
      "tensor([0.67436], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00188], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.813      0.887      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.61G    0.02688    0.02407   0.002544        168        640: 1\n",
      "tensor([0.70082], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00188], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906       0.84      0.886      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.61G    0.02693    0.02386   0.002392        175        640: 1\n",
      "tensor([0.76525], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00188], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.821      0.889      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.61G    0.02651     0.0239   0.002469        139        640: 1\n",
      "tensor([0.73887], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.824      0.889      0.624\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.61G    0.02643     0.0232   0.002411        117        640: 1\n",
      "tensor([0.63360], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.821      0.875      0.613\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.61G     0.0266    0.02351   0.002362        129        640: 1\n",
      "tensor([0.65361], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.817      0.884      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.61G    0.02642    0.02367   0.002511        109        640: 1\n",
      "tensor([0.62612], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.838      0.887      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.61G    0.02638    0.02371   0.002338        154        640: 1\n",
      "tensor([0.77817], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.804      0.887      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.61G    0.02613    0.02349   0.002368        119        640: 1\n",
      "tensor([0.64940], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.933       0.82      0.884      0.621\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.61G    0.02609    0.02293   0.002364        153        640: 1\n",
      "tensor([0.71218], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.834      0.885      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.61G    0.02606    0.02347   0.002359        116        640: 1\n",
      "tensor([0.62373], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.838      0.888      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.61G    0.02566    0.02283   0.002365        141        640: 1\n",
      "tensor([0.67358], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.833      0.892      0.632\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.61G    0.02575    0.02303     0.0023        175        640: 1\n",
      "tensor([0.82253], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.824      0.892      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.61G     0.0259    0.02307   0.002336        161        640: 1\n",
      "tensor([0.73112], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922       0.83      0.891      0.632\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.61G    0.02564    0.02268   0.002234        114        640: 1\n",
      "tensor([0.61011], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.824      0.888      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.61G    0.02575    0.02313   0.002298        141        640: 1\n",
      "tensor([0.69109], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.826      0.884      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.61G    0.02553      0.023   0.002227        133        640: 1\n",
      "tensor([0.58551], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.841      0.891      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.61G     0.0253    0.02254   0.002188        159        640: 1\n",
      "tensor([0.75201], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.815      0.892      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.61G    0.02543    0.02254   0.002259        122        640: 1\n",
      "tensor([0.54742], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913       0.83      0.891      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.61G    0.02543    0.02276   0.002202        137        640: 1\n",
      "tensor([0.66619], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.831      0.891      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.61G    0.02531    0.02235   0.002219        137        640: 1\n",
      "tensor([0.64689], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00182], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.837      0.892      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.61G    0.02476     0.0219   0.002253        161        640: 1\n",
      "tensor([0.73790], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00182], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.865       0.89      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.61G    0.02488    0.02213   0.002199        181        640: 1\n",
      "tensor([0.73102], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.835       0.89      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.61G    0.02471    0.02197   0.002128        149        640: 1\n",
      "tensor([0.62234], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.823      0.893      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.61G    0.02464    0.02207    0.00207        118        640: 1\n",
      "tensor([0.60183], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.845      0.894      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.61G    0.02451    0.02187   0.002094        178        640: 1\n",
      "tensor([0.73942], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.848      0.891      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.61G    0.02444    0.02183   0.002055        140        640: 1\n",
      "tensor([0.63718], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.856      0.896      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.61G    0.02447     0.0219   0.002126        119        640: 1\n",
      "tensor([0.51720], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.843      0.893      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.61G    0.02442    0.02203   0.002043        114        640: 1\n",
      "tensor([0.50637], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.851      0.889      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.61G     0.0245    0.02166   0.002116        117        640: 1\n",
      "tensor([0.53587], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.849      0.893      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.61G    0.02432    0.02176   0.002038        118        640: 1\n",
      "tensor([0.55467], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.851      0.895       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.61G    0.02423    0.02146   0.001968        115        640: 1\n",
      "tensor([0.56866], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.837       0.89      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.61G    0.02398    0.02124   0.001994        159        640: 1\n",
      "tensor([0.73645], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.839      0.887      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.61G    0.02403    0.02147   0.002056        165        640: 1\n",
      "tensor([0.68350], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.835      0.889      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.61G     0.0239    0.02115   0.002026        126        640: 1\n",
      "tensor([0.57254], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.855      0.889      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.61G     0.0238    0.02113   0.002033        112        640: 1\n",
      "tensor([0.58140], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.848      0.889      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.61G    0.02358    0.02096   0.001881        121        640: 1\n",
      "tensor([0.58210], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.856       0.89      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.61G    0.02387    0.02093   0.002018        195        640: 1\n",
      "tensor([0.63439], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.846      0.892      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.61G    0.02387    0.02107   0.001942        101        640: 1\n",
      "tensor([0.57391], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.857      0.892      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.61G    0.02357    0.02093      0.002        137        640: 1\n",
      "tensor([0.56886], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.855      0.895      0.653\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.61G    0.02359    0.02108   0.001906        115        640: 1\n",
      "tensor([0.49909], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.837      0.892      0.654\n",
      "\n",
      "100 epochs completed in 1.099 hours.\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_SI_0.005/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_SI_0.005/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0_to_fog_1.0_SI_0.005/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.837      0.892      0.654\n",
      "                   Car       1048       4012      0.947      0.916      0.966      0.791\n",
      "                   Van       1048        431      0.929      0.917      0.961      0.784\n",
      "                 Truck       1048        166      0.929       0.94      0.959      0.777\n",
      "                  Tram       1048         56      0.907      0.911       0.95       0.73\n",
      "            Pedestrian       1048        618      0.879      0.722      0.826      0.463\n",
      "        Person_sitting       1048         20          1      0.641      0.724      0.463\n",
      "               Cyclist       1048        234      0.872      0.808      0.864      0.585\n",
      "                  Misc       1048        138      0.911      0.841      0.888      0.642\n",
      "Results saved to \u001b[1mruns/train/fog_0_to_fog_1.0_SI_0.005\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0_to_fog_1.0_SI_0.005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/e252c729602a420e8610ece3ebbe5389\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9315142975336288\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 205.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9657087442907524\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7907972406864735\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.947294471528492\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9162512462612163\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3676.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8388301713639507\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 28.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8635058134399302\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5847842331593861\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8724651337554563\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.8076923076923077\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 189.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8743250424823747\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8883247890490062\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.6420757779906694\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.9108931287477329\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.8405797101449275\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 116.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7925103873691179\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 62.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.8259305981283025\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.463392103123949\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8787531513404722\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7216828478964401\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 446.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7809201376123107\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.724307139401479\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.4626773291064647\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.640581607248274\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9088661090678181\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 5.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9504011790791503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.730171590418422\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.907025418499416\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9107142857142857\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9344856545144432\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9592136321983521\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.776949966406929\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9292711249475687\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9397590361445783\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 156.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9231356389097299\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 30.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9611884230685368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7840303991326075\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9294431750877677\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9169131360897399\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 395.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5262854099273682, 2.1990065574645996)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.21851911021107773, 0.8965074493126798)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.11564171930905776, 0.6543387119115156)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.4535713817138584, 0.9332436902370141)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.215440243386976, 0.8648578150653652)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.023565657436847687, 0.03831126540899277)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0018807189771905541, 0.007072508335113525)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.020931383594870567, 0.0343809500336647)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.024960843846201897, 0.05483492091298103)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0028617067728191614, 0.028329098597168922)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03932727500796318, 0.10985703766345978)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0_to_fog_1.0_SI_0.005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/e252c729602a420e8610ece3ebbe5389\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0_to_fog_1.0_SI_0.005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.82 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 205 metrics, params and output messages\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--SI_enable \\\n",
    "--SI_pt ./runs/train/fog_02/weights/si.pt \\\n",
    "--SI_lambda 5e-3 \\\n",
    "--name fog_0_to_fog_1.0_SI_0.005 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed242a93-52a1-4b9a-a0a7-34cf56b1cfc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_SI_0.005/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 cbe9b398 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198       0.83      0.645      0.744      0.466\n",
      "                   Car       2244       8711      0.912      0.786      0.891      0.638\n",
      "                   Van       2244        861      0.812      0.677      0.768      0.529\n",
      "                 Truck       2244        333      0.902      0.802      0.871      0.636\n",
      "                  Tram       2244        138      0.924      0.667      0.804      0.481\n",
      "            Pedestrian       2244       1286      0.744      0.615      0.688      0.361\n",
      "        Person_sitting       2244         89      0.675      0.596      0.664      0.315\n",
      "               Cyclist       2244        496      0.865      0.466      0.585      0.337\n",
      "                  Misc       2244        284      0.809      0.556      0.683      0.426\n",
      "Speed: 0.1ms pre-process, 0.9ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp79\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_SI_0.005/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c4efa97-59c6-424e-b4a1-7deceb13ab63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47a79eec-55ee-4fe5-a850-3aca18fff67a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7a553ad-58e1-4dd8-91da-2977e35b19dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0_to_fog_1.0_ewc_0.001, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=runs/train/fog_02/weights/fisher.pt, ewc_lambda=0.001, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 cbe9b398 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/ebe07d9f239f408cb5bacb4f8d6389b4\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val... 1048 images, 0 backg\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/val.cache\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0_to_fog_1.0_ewc_0.001/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0_to_fog_1.0_ewc_0.001\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.53G    0.03485    0.03429   0.006985        128        640: 1\n",
      "tensor([0.89333], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.652       0.23      0.262      0.153\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.53G    0.03377    0.03049   0.005148        133        640: 1\n",
      "tensor([0.96951], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.524      0.194      0.197      0.111\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.53G    0.03665    0.03348   0.006631        131        640: 1\n",
      "tensor([1.08227], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.475      0.143      0.139     0.0786\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.53G     0.0389    0.03516   0.007611        108        640: 1\n",
      "tensor([0.86673], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.613        0.3      0.341      0.181\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.53G    0.03809    0.03314   0.006215        156        640: 1\n",
      "tensor([0.94194], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.76      0.472      0.562      0.313\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.53G    0.03703    0.03237   0.005836        123        640: 1\n",
      "tensor([0.90550], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.689      0.509      0.568      0.312\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.53G    0.03641     0.0315   0.005256        174        640: 1\n",
      "tensor([1.03341], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.659      0.456       0.49      0.275\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.53G    0.03613    0.03091    0.00513        166        640: 1\n",
      "tensor([1.09926], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.815      0.634      0.721      0.431\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.53G    0.03544    0.03102   0.004921        152        640: 1\n",
      "tensor([0.94738], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.796      0.584      0.671      0.404\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.53G    0.03494    0.03074   0.004815        136        640: 1\n",
      "tensor([0.89109], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.775      0.652      0.732      0.436\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.53G    0.03487    0.03048   0.004699        134        640: 1\n",
      "tensor([0.87155], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.859      0.672      0.764      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.53G    0.03423    0.02976   0.004476        182        640: 1\n",
      "tensor([0.96081], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.765      0.508      0.587      0.342\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.53G    0.03414    0.02993   0.004312        128        640: 1\n",
      "tensor([0.76022], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.801      0.672      0.741      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.53G     0.0336    0.02921   0.004153        112        640: 1\n",
      "tensor([0.92403], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.728      0.491      0.561      0.339\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.53G    0.03354    0.02956    0.00429        151        640: 1\n",
      "tensor([0.86311], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.564      0.666      0.396\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.53G    0.03335    0.02928   0.003987        132        640: 1\n",
      "tensor([0.83602], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.728      0.818      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.53G    0.03266    0.02871   0.003826        131        640: 1\n",
      "tensor([0.80726], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.761      0.838      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.53G     0.0331    0.02877   0.003934        159        640: 1\n",
      "tensor([0.90633], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.712      0.814      0.496\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.53G    0.03251    0.02838   0.003847        125        640: 1\n",
      "tensor([0.74946], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.686      0.772      0.461\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.53G    0.03253    0.02876   0.003796         88        640: 1\n",
      "tensor([0.69298], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.81      0.638      0.717      0.435\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.53G    0.03215    0.02797   0.003764        137        640: 1\n",
      "tensor([0.97018], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.724      0.815      0.516\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.53G    0.03176    0.02826   0.003592        166        640: 1\n",
      "tensor([0.91133], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.768      0.846      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.53G    0.03145    0.02782   0.003538        161        640: 1\n",
      "tensor([0.87526], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.763      0.822      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.53G    0.03161    0.02748   0.003513        118        640: 1\n",
      "tensor([0.77070], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.765      0.841      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.53G    0.03137    0.02751   0.003505        151        640: 1\n",
      "tensor([0.86176], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.695      0.807      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.53G    0.03123    0.02767   0.003431        133        640: 1\n",
      "tensor([0.80384], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.727       0.82      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.53G    0.03107    0.02744   0.003386        154        640: 1\n",
      "tensor([0.93751], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.766       0.84      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.53G    0.03097    0.02761   0.003582        122        640: 1\n",
      "tensor([0.76383], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.852      0.791      0.837       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.53G    0.03086    0.02745    0.00332        123        640: 1\n",
      "tensor([0.65575], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868       0.72       0.81      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.53G    0.03048    0.02689    0.00325        127        640: 1\n",
      "tensor([0.68310], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.785      0.855      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.53G    0.03015    0.02598   0.003187        127        640: 1\n",
      "tensor([0.71365], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.766       0.84      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.53G    0.03021    0.02654   0.003171        122        640: 1\n",
      "tensor([0.78797], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.792      0.868      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.53G    0.03033    0.02683   0.003127        146        640: 1\n",
      "tensor([0.83105], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912       0.79      0.866      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.53G    0.02968     0.0262   0.003147        202        640: 1\n",
      "tensor([0.91004], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.783      0.858       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.53G    0.02997    0.02617   0.003177         94        640: 1\n",
      "tensor([0.66078], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.793       0.86      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.53G    0.02987    0.02611   0.003068        152        640: 1\n",
      "tensor([0.81083], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.819      0.875      0.586\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.53G    0.02963    0.02594   0.003042        123        640: 1\n",
      "tensor([0.70696], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.763      0.845      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.53G    0.02948    0.02608   0.003127        162        640: 1\n",
      "tensor([0.76238], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.793      0.862       0.58\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.53G     0.0296    0.02632   0.003141        161        640: 1\n",
      "tensor([0.79268], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.712      0.804      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.53G    0.02957    0.02625   0.003069        122        640: 1\n",
      "tensor([0.68135], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.771      0.867       0.58\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.53G    0.02936     0.0258   0.003025        126        640: 1\n",
      "tensor([0.67246], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.782      0.871      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.53G    0.02889    0.02572   0.003029         90        640: 1\n",
      "tensor([0.62357], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.809      0.876      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.53G    0.02877    0.02552    0.00292        118        640: 1\n",
      "tensor([0.73778], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.792      0.865      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.53G    0.02888     0.0256   0.002734        157        640: 1\n",
      "tensor([0.78480], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.796      0.867      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.53G    0.02874    0.02552   0.002885        104        640: 1\n",
      "tensor([0.54044], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.774      0.854      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.53G    0.02858    0.02575    0.00272        157        640: 1\n",
      "tensor([0.72981], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.792      0.875      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.53G    0.02852     0.0251   0.002708        108        640: 1\n",
      "tensor([0.56639], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913        0.8      0.876      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.53G    0.02847     0.0251   0.002733        159        640: 1\n",
      "tensor([0.76302], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.801      0.875      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.53G    0.02793    0.02493   0.002682        118        640: 1\n",
      "tensor([0.69044], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.808      0.867      0.594\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.53G     0.0282    0.02537   0.002705        176        640: 1\n",
      "tensor([0.89418], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.803      0.876      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.53G    0.02816    0.02498   0.002746        130        640: 1\n",
      "tensor([0.67636], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.802      0.878      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.53G    0.02777    0.02502   0.002579        178        640: 1\n",
      "tensor([0.89694], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.805      0.877      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.53G     0.0277    0.02465   0.002646        148        640: 1\n",
      "tensor([0.68493], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.811      0.885      0.606\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.53G    0.02756    0.02452   0.002586        115        640: 1\n",
      "tensor([0.66378], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.812      0.876      0.608\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.53G    0.02774    0.02452    0.00267        124        640: 1\n",
      "tensor([0.66597], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.814      0.876      0.608\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.53G     0.0272    0.02408   0.002581        163        640: 1\n",
      "tensor([0.69831], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.786      0.877      0.613\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.53G    0.02731    0.02444   0.002542        200        640: 1\n",
      "tensor([0.79083], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.811       0.88      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.53G     0.0272    0.02443   0.002563        141        640: 1\n",
      "tensor([0.68309], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.823      0.882      0.606\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.53G    0.02722    0.02426   0.002616        146        640: 1\n",
      "tensor([0.68539], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913       0.81      0.888      0.617\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.53G      0.027    0.02419   0.002546        168        640: 1\n",
      "tensor([0.71844], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.825      0.882      0.617\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.53G    0.02691     0.0239   0.002452        175        640: 1\n",
      "tensor([0.78945], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.792      0.882      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.53G    0.02668    0.02403   0.002525        139        640: 1\n",
      "tensor([0.75108], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.808      0.879      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.53G    0.02653    0.02329   0.002403        117        640: 1\n",
      "tensor([0.63533], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.804      0.878      0.613\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.53G    0.02661    0.02356   0.002413        129        640: 1\n",
      "tensor([0.67535], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909       0.82       0.88      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.53G    0.02649    0.02367    0.00247        109        640: 1\n",
      "tensor([0.64738], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.799      0.876       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.53G    0.02647    0.02376   0.002329        154        640: 1\n",
      "tensor([0.76791], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.823      0.884      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.53G    0.02613    0.02355   0.002379        119        640: 1\n",
      "tensor([0.64423], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.816      0.884      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.53G    0.02611    0.02297   0.002381        153        640: 1\n",
      "tensor([0.70567], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.833      0.882      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.53G    0.02612    0.02351   0.002392        116        640: 1\n",
      "tensor([0.61481], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.827      0.885      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.53G    0.02569    0.02283   0.002378        141        640: 1\n",
      "tensor([0.70060], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.814      0.882      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.53G     0.0258    0.02309   0.002327        175        640: 1\n",
      "tensor([0.83723], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.808      0.885      0.625\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.53G    0.02591    0.02307   0.002324        161        640: 1\n",
      "tensor([0.72444], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.808      0.892      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.53G     0.0257    0.02277   0.002229        114        640: 1\n",
      "tensor([0.61767], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.812      0.884      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.53G    0.02584    0.02325    0.00236        141        640: 1\n",
      "tensor([0.71377], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.834      0.881      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.53G    0.02567    0.02318   0.002263        133        640: 1\n",
      "tensor([0.59471], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.823      0.888      0.632\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.53G    0.02537    0.02257   0.002203        159        640: 1\n",
      "tensor([0.75962], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.814      0.888      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.53G    0.02553    0.02261   0.002286        122        640: 1\n",
      "tensor([0.58386], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915       0.83      0.888      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.53G    0.02549    0.02286   0.002218        137        640: 1\n",
      "tensor([0.66611], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.826      0.886      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.53G    0.02539    0.02243   0.002242        137        640: 1\n",
      "tensor([0.66381], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.825      0.883      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.53G    0.02485    0.02201    0.00223        161        640: 1\n",
      "tensor([0.73512], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.822      0.889      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.53G    0.02506    0.02252   0.002131        154        640: 1\n",
      "tensor([0.62355], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.839      0.893      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.53G    0.02491    0.02215   0.002156        181        640: 1\n",
      "tensor([0.73874], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.839       0.89      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.53G    0.02479    0.02203   0.002147        149        640: 1\n",
      "tensor([0.63964], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.815      0.886      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.53G    0.02471    0.02211   0.002059        118        640: 1\n",
      "tensor([0.60494], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.822      0.893      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.53G    0.02456    0.02192   0.002086        178        640: 1\n",
      "tensor([0.74081], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.825      0.894      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.53G    0.02448    0.02188    0.00205        140        640: 1\n",
      "tensor([0.65730], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.841      0.892       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.53G    0.02455    0.02196   0.002109        119        640: 1\n",
      "tensor([0.53180], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9       0.84      0.889      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.53G    0.02448     0.0221    0.00205        114        640: 1\n",
      "tensor([0.51729], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.838      0.891      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.53G    0.02457    0.02174   0.002105        117        640: 1\n",
      "tensor([0.54020], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.832      0.895      0.652\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.53G    0.02439    0.02184    0.00202        118        640: 1\n",
      "tensor([0.54412], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.824      0.892       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.53G    0.02425    0.02145   0.001976        115        640: 1\n",
      "tensor([0.57904], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.835      0.892      0.653\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.53G      0.024    0.02124   0.001982        159        640: 1\n",
      "tensor([0.74714], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.834       0.89      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.53G    0.02403     0.0215   0.002031        165        640: 1\n",
      "tensor([0.69143], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.848      0.892      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.53G    0.02391    0.02119   0.002021        126        640: 1\n",
      "tensor([0.57762], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906       0.84      0.891      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.53G    0.02383     0.0212   0.002028        112        640: 1\n",
      "tensor([0.58417], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.837      0.888      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.53G    0.02363    0.02101   0.001892        121        640: 1\n",
      "tensor([0.58070], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.829      0.889      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.53G     0.0239    0.02099   0.002023        195        640: 1\n",
      "tensor([0.63479], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.834       0.89       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.53G    0.02386    0.02111   0.001952        101        640: 1\n",
      "tensor([0.60514], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.833      0.889      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.53G    0.02359    0.02097   0.001971        137        640: 1\n",
      "tensor([0.57542], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.837      0.889      0.651\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.53G    0.02366    0.02109   0.001911        115        640: 1\n",
      "tensor([0.48577], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.842      0.888       0.65\n",
      "\n",
      "100 epochs completed in 1.432 hours.\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_ewc_0.001/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_ewc_0.001/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0_to_fog_1.0_ewc_0.001/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.835      0.892      0.653\n",
      "                   Car       1048       4012      0.944       0.92      0.966      0.788\n",
      "                   Van       1048        431      0.942      0.934      0.963      0.787\n",
      "                 Truck       1048        166      0.933      0.904       0.97      0.786\n",
      "                  Tram       1048         56      0.928      0.946      0.952      0.745\n",
      "            Pedestrian       1048        618      0.881      0.714      0.822       0.45\n",
      "        Person_sitting       1048         20      0.983       0.65      0.728      0.479\n",
      "               Cyclist       1048        234      0.882      0.801      0.867      0.571\n",
      "                  Misc       1048        138      0.899      0.812      0.871      0.618\n",
      "Results saved to \u001b[1mruns/train/fog_0_to_fog_1.0_ewc_0.001\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m The process of logging environment details (conda environment, git patch) is underway. Please be patient as this may take some time.\n",
      "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Failed to complete logging of all environment details (conda environment, git patch)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0_to_fog_1.0_ewc_0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/ebe07d9f239f408cb5bacb4f8d6389b4\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9319637955275086\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 218.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9657741043830013\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7876890328091825\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9442533507813318\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9199900299102692\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3691.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8398620919978287\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 25.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8666404214827279\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5705640701696886\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8823516006540779\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.8012767314761617\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 187.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8531203364090637\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8712622142173773\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.6183179852822176\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8991250863766993\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.8115942028985508\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 112.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7885678629769909\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 59.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.8220227079504009\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.4498769755630218\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8811482500464537\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7135922330097088\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 441.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7826649368957844\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.7283444954825439\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.4794948242084735\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9833708272732663\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.65\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9368805602447791\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9522107414464082\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7446316863145044\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.927523274480885\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9464285714285714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9178336822449237\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9696309761118733\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7858789798300647\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9325075669385132\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9036144578313253\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 150.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9376339068386466\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 25.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9631081569551553\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7874806870151606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9415150754104615\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9337846052635087\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 402.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5393362045288086, 2.199005603790283)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.13937058218031975, 0.8954008494716537)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.07860393500928435, 0.6529910453719143)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.47522577567705226, 0.9360066160331552)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.14252581561912786, 0.847792551430782)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.02358825132250786, 0.03889595344662666)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0018924119649454951, 0.007611403241753578)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.020965153351426125, 0.03515535965561867)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.025001490488648415, 0.06259670108556747)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0028835751581937075, 0.034927647560834885)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03944651409983635, 0.1317080855369568)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0_to_fog_1.0_ewc_0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/ebe07d9f239f408cb5bacb4f8d6389b4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0_to_fog_1.0_ewc_0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.81 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Failed to log run in comet.com\n"
     ]
    }
   ],
   "source": [
    "# ewc\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--ewc_pt runs/train/fog_02/weights/fisher.pt \\\n",
    "--ewc_lambda 1e-3 \\\n",
    "--name fog_0_to_fog_1.0_ewc_0.001 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a41e27cb-9e54-47f8-a1cc-bb37b8e03d4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_ewc_0.001L2/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 cbe9b398 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.858       0.63      0.743      0.462\n",
      "                   Car       2244       8711      0.912       0.78       0.89      0.641\n",
      "                   Van       2244        861      0.827      0.672      0.772      0.536\n",
      "                 Truck       2244        333       0.92      0.754      0.853      0.619\n",
      "                  Tram       2244        138      0.954      0.605      0.783      0.453\n",
      "            Pedestrian       2244       1286      0.786      0.616      0.691      0.363\n",
      "        Person_sitting       2244         89       0.71      0.596      0.676      0.329\n",
      "               Cyclist       2244        496      0.899      0.464      0.602      0.336\n",
      "                  Misc       2244        284      0.854      0.556      0.679      0.419\n",
      "Speed: 0.1ms pre-process, 0.9ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp82\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_ewc_0.001L2/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57a45117-e916-4aa7-9640-054642bbc6ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f5afc7b-d105-41d9-9475-3f6a1e5b08da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2942164d-c0ea-4e82-9934-c1dedb5381b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d644c19b-27ff-4dc1-b8dc-e0242b354922",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f738573-a4e3-4720-ae11-d7ff48fef4ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8b65d98-b089-4366-a517-d4f2b23b77cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开始回放。还是以1.0作为验证集\n",
    "from fog_test.for_different_strength import mix_dataset\n",
    "origin_ratio = {\n",
    "    '1.0':0.8,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )\n",
    "\n",
    "# val_fogged_strength = 1.0\n",
    "# # 替换验证集\n",
    "# update_testsets = f\" \\\n",
    "# rm ../datasets/kitti/images/val/* &&\\\n",
    "# cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "# echo 'Val set updated successfully!' \\\n",
    "# \" \n",
    "# !{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec90f477-7ef1-477b-b0bc-f492396a891f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0_to_fog_1.0_replay_2:8, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 cbe9b398 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': HTTP/2 stream 1 was not closed cleanly before end of the underlying stream\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/d3ded5c21585484ca6bee052f69311f0\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0_to_fog_1.0_replay_2:8/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0_to_fog_1.0_replay_2:8\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.65G    0.03465    0.03402   0.006905        128        640: 1\n",
      "tensor([0.88015], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.656       0.34      0.385       0.22\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.65G    0.03275    0.02988   0.005108        170        640:  \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda information\n",
      "       1/99      3.65G    0.03355    0.03012   0.005006        133        640: 1\n",
      "tensor([0.97310], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.67      0.261      0.301      0.172\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.65G    0.03562    0.03241   0.006071        131        640: 1\n",
      "tensor([1.01914], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.59      0.236      0.229      0.126\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.65G    0.03818    0.03428   0.007062        108        640: 1\n",
      "tensor([0.84068], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.457      0.265      0.261       0.14\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.65G    0.03774     0.0331   0.006269        156        640: 1\n",
      "tensor([0.96250], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.707      0.573      0.625      0.339\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.65G    0.03702    0.03195   0.005496        123        640: 1\n",
      "tensor([0.89485], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.771      0.565      0.657      0.366\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.65G    0.03605    0.03128   0.005197        174        640: 1\n",
      "tensor([1.03346], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.769      0.607      0.695       0.39\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.65G    0.03562    0.03043   0.004914        166        640: 1\n",
      "tensor([1.07232], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.682      0.774      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.65G    0.03523    0.03069   0.004691        152        640: 1\n",
      "tensor([0.92899], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.819      0.664      0.743      0.455\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.65G    0.03477     0.0305   0.004752        136        640: 1\n",
      "tensor([0.86833], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.817      0.671      0.762      0.454\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.65G    0.03434    0.03009   0.004439        134        640: 1\n",
      "tensor([0.87275], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.677      0.783      0.486\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.65G    0.03397    0.02937   0.004306        182        640: 1\n",
      "tensor([0.89655], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.853      0.728      0.794      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.65G    0.03371    0.02963   0.004196        128        640: 1\n",
      "tensor([0.78809], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.82      0.703      0.774      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.65G    0.03325    0.02889   0.004076        112        640: 1\n",
      "tensor([0.86767], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.838      0.647      0.736       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.65G    0.03305    0.02922    0.00409        151        640: 1\n",
      "tensor([0.81935], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.683      0.793      0.485\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.65G    0.03293     0.0291   0.003954        132        640: 1\n",
      "tensor([0.86410], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.775      0.846      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.65G    0.03286    0.02873   0.003964        131        640: 1\n",
      "tensor([0.79598], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.859      0.751      0.823      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.65G    0.03278    0.02852    0.00383        159        640: 1\n",
      "tensor([0.92331], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.761       0.84      0.523\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.65G    0.03225    0.02818   0.003714        125        640: 1\n",
      "tensor([0.73726], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.837      0.773       0.84      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.65G     0.0323     0.0285   0.003801         88        640: 1\n",
      "tensor([0.68218], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858      0.752      0.826      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.65G    0.03196    0.02775   0.003704        137        640: 1\n",
      "tensor([0.89829], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.766      0.841      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.65G    0.03141    0.02806   0.003552        166        640: 1\n",
      "tensor([0.90173], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.752      0.849      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.65G    0.03165    0.02786   0.003537        161        640: 1\n",
      "tensor([0.89474], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.738      0.842      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.65G    0.03153     0.0275   0.003543        118        640: 1\n",
      "tensor([0.75817], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.762      0.842      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.65G    0.03122    0.02747   0.003475        151        640: 1\n",
      "tensor([0.84909], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.772      0.854      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.65G    0.03092    0.02757    0.00334        133        640: 1\n",
      "tensor([0.78229], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.775      0.851      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.65G    0.03094    0.02722   0.003371        154        640: 1\n",
      "tensor([0.90857], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.768      0.855      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.65G    0.03073    0.02736   0.003451        122        640: 1\n",
      "tensor([0.72911], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.765      0.842      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.65G    0.03077    0.02726   0.003287        123        640: 1\n",
      "tensor([0.68825], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.791      0.863       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.65G    0.03038    0.02679   0.003221        127        640: 1\n",
      "tensor([0.66590], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.758      0.849      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.65G    0.02999    0.02585   0.003167        127        640: 1\n",
      "tensor([0.70626], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.781      0.853      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.65G    0.03017    0.02652   0.003235        122        640: 1\n",
      "tensor([0.78683], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909       0.78       0.86      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.65G    0.02997    0.02673   0.003104        146        640: 1\n",
      "tensor([0.80151], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881       0.77      0.847      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.65G    0.03005    0.02616   0.003133        202        640: 1\n",
      "tensor([0.93136], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.781      0.851      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.65G    0.02977    0.02608   0.003124         94        640: 1\n",
      "tensor([0.64195], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.784      0.855      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.65G    0.02955    0.02594   0.003064        152        640: 1\n",
      "tensor([0.84387], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.793      0.872      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.65G    0.02948    0.02594   0.003096        123        640: 1\n",
      "tensor([0.68995], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.782      0.862      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.65G    0.02944    0.02598   0.003093        162        640: 1\n",
      "tensor([0.75585], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.781      0.861      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.65G    0.02935    0.02617   0.003116        161        640: 1\n",
      "tensor([0.77976], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.803      0.867      0.588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.65G    0.02933    0.02608    0.00295        122        640: 1\n",
      "tensor([0.67741], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.804      0.867      0.586\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.65G    0.02923    0.02572   0.003035        126        640: 1\n",
      "tensor([0.66285], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.794      0.869      0.593\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.65G     0.0287     0.0256   0.002988         90        640: 1\n",
      "tensor([0.61396], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.823      0.876      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.65G    0.02877    0.02547   0.002822        118        640: 1\n",
      "tensor([0.75258], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.799      0.868      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.65G    0.02874    0.02547    0.00275        157        640: 1\n",
      "tensor([0.78091], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.783      0.867      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.65G    0.02871    0.02551   0.002814        104        640: 1\n",
      "tensor([0.55160], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.797      0.864      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.65G    0.02839    0.02562   0.002741        157        640: 1\n",
      "tensor([0.72420], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.811      0.877      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.65G    0.02825    0.02502    0.00271        108        640: 1\n",
      "tensor([0.57114], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.824      0.877      0.605\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.65G    0.02844    0.02505   0.002779        159        640: 1\n",
      "tensor([0.75464], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.786      0.868      0.594\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.65G    0.02796    0.02494   0.002699        118        640: 1\n",
      "tensor([0.68845], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.778      0.855      0.585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.65G    0.02815    0.02531   0.002718        176        640: 1\n",
      "tensor([0.85195], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.813       0.87      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.65G     0.0281    0.02486   0.002719        130        640: 1\n",
      "tensor([0.67060], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.795      0.873      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.65G    0.02766    0.02496    0.00265        178        640: 1\n",
      "tensor([0.85221], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.797      0.867      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.65G    0.02767     0.0246   0.002633        148        640: 1\n",
      "tensor([0.68666], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.792      0.873      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.65G    0.02748    0.02443   0.002547        115        640: 1\n",
      "tensor([0.63108], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.813      0.876      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.65G    0.02758    0.02439   0.002638        124        640: 1\n",
      "tensor([0.61381], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.785      0.871      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.65G    0.02726     0.0241   0.002618        163        640: 1\n",
      "tensor([0.70540], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.799      0.874      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.65G    0.02715    0.02442   0.002563        200        640: 1\n",
      "tensor([0.78292], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.796      0.873      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.65G    0.02718    0.02437   0.002525        141        640: 1\n",
      "tensor([0.66540], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.797      0.889      0.624\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.65G    0.02708    0.02419   0.002574        146        640: 1\n",
      "tensor([0.68434], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.802      0.884      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.65G    0.02698    0.02417   0.002561        168        640: 1\n",
      "tensor([0.70933], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.817       0.88      0.624\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.65G     0.0269    0.02393   0.002422        175        640: 1\n",
      "tensor([0.76302], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.806      0.878      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.65G    0.02659    0.02392   0.002499        139        640: 1\n",
      "tensor([0.76122], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.815      0.884      0.617\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.65G    0.02647    0.02325   0.002424        117        640: 1\n",
      "tensor([0.62109], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.807      0.883      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.65G    0.02665    0.02353   0.002401        129        640: 1\n",
      "tensor([0.66593], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.827      0.888      0.625\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.65G    0.02636    0.02363   0.002472        109        640: 1\n",
      "tensor([0.63656], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.807      0.869      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.65G    0.02638    0.02373   0.002378        154        640: 1\n",
      "tensor([0.75122], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902       0.84      0.884      0.624\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.65G    0.02618    0.02353   0.002381        119        640: 1\n",
      "tensor([0.62566], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.805       0.88      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.65G    0.02611    0.02298   0.002393        153        640: 1\n",
      "tensor([0.73049], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.823      0.883      0.625\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.65G    0.02606    0.02347   0.002378        116        640: 1\n",
      "tensor([0.61800], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919       0.81      0.874      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.65G    0.02568    0.02279   0.002374        141        640: 1\n",
      "tensor([0.68774], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.812      0.878      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.65G    0.02587    0.02307   0.002344        175        640: 1\n",
      "tensor([0.79435], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.813      0.881      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.65G    0.02589    0.02312   0.002343        161        640: 1\n",
      "tensor([0.70634], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.823      0.881      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.65G    0.02569    0.02273   0.002222        114        640: 1\n",
      "tensor([0.60468], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.811      0.879      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.65G    0.02586    0.02322   0.002313        141        640: 1\n",
      "tensor([0.69815], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.812      0.883      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.65G    0.02562    0.02309   0.002256        133        640: 1\n",
      "tensor([0.59600], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.836      0.885      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.65G    0.02535    0.02257   0.002193        159        640: 1\n",
      "tensor([0.75631], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.847      0.892       0.64\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.65G    0.02553    0.02262   0.002273        122        640: 1\n",
      "tensor([0.57474], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918       0.83      0.884       0.64\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.65G    0.02545    0.02287   0.002192        137        640: 1\n",
      "tensor([0.65295], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.815      0.887      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.65G     0.0254    0.02243   0.002233        137        640: 1\n",
      "tensor([0.66324], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.826      0.884      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.65G    0.02483    0.02199   0.002227        161        640: 1\n",
      "tensor([0.74275], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.812      0.886      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.65G    0.02506    0.02244   0.002102        154        640: 1\n",
      "tensor([0.62185], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.829      0.892      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.65G    0.02487    0.02212   0.002167        181        640: 1\n",
      "tensor([0.74677], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.815      0.887      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.65G    0.02474    0.02199   0.002147        149        640: 1\n",
      "tensor([0.61015], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.825      0.888      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.65G    0.02472    0.02213   0.002073        118        640: 1\n",
      "tensor([0.59172], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.826       0.89      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.65G    0.02454    0.02193   0.002084        178        640: 1\n",
      "tensor([0.74762], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.829      0.882      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.65G     0.0245    0.02186   0.002052        140        640: 1\n",
      "tensor([0.65665], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909       0.83      0.888       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.65G    0.02454    0.02195    0.00213        119        640: 1\n",
      "tensor([0.54061], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.836      0.893      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.65G    0.02447    0.02208   0.002045        114        640: 1\n",
      "tensor([0.50802], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.842      0.889       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.65G    0.02454    0.02174   0.002122        117        640: 1\n",
      "tensor([0.53531], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.823      0.892      0.651\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.65G    0.02442    0.02184   0.002043        118        640: 1\n",
      "tensor([0.55272], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.834      0.891       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.65G    0.02423    0.02145   0.001971        115        640: 1\n",
      "tensor([0.58525], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.843      0.887      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.65G    0.02406     0.0213   0.002001        159        640: 1\n",
      "tensor([0.72285], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.849      0.889       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.65G    0.02404    0.02154   0.002041        165        640: 1\n",
      "tensor([0.68027], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.835      0.888      0.651\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.65G    0.02396    0.02124   0.002015        126        640: 1\n",
      "tensor([0.56590], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.835      0.888      0.653\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.65G    0.02386    0.02121   0.002058        112        640: 1\n",
      "tensor([0.58808], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.831      0.885      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.65G    0.02364    0.02104   0.001887        121        640: 1\n",
      "tensor([0.60384], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.829      0.884      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.65G    0.02394      0.021    0.00201        195        640: 1\n",
      "tensor([0.62950], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.834      0.883      0.651\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.65G    0.02389    0.02113   0.001969        101        640: 1\n",
      "tensor([0.59611], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.837      0.889      0.656\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.65G    0.02362    0.02098    0.00199        137        640: 1\n",
      "tensor([0.56425], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.822      0.887      0.654\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.65G    0.02371    0.02113   0.001907        115        640: 1\n",
      "tensor([0.49654], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.822      0.888      0.656\n",
      "\n",
      "100 epochs completed in 0.983 hours.\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_replay_2:8/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_replay_2:8/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0_to_fog_1.0_replay_2:8/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.823      0.889      0.656\n",
      "                   Car       1048       4012      0.956      0.907      0.965      0.788\n",
      "                   Van       1048        431      0.947      0.919      0.961       0.78\n",
      "                 Truck       1048        166      0.944      0.912      0.965      0.788\n",
      "                  Tram       1048         56      0.924      0.946      0.945      0.737\n",
      "            Pedestrian       1048        618      0.892      0.684      0.818      0.453\n",
      "        Person_sitting       1048         20      0.919        0.6      0.713      0.485\n",
      "               Cyclist       1048        234      0.888      0.811      0.865       0.59\n",
      "                  Misc       1048        138      0.911      0.804      0.876      0.624\n",
      "Results saved to \u001b[1mruns/train/fog_0_to_fog_1.0_replay_2:8\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0_to_fog_1.0_replay_2:8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/d3ded5c21585484ca6bee052f69311f0\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9311830971022883\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 167.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9653870450540418\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7878009256820432\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9561433609242695\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.907492861755075\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3641.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8473909910220138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 24.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.864980248544384\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5899447633181946\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8876820291625591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.8105986918247454\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 190.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8545339911049765\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8761071971373379\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.623977357236137\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.9113994963473354\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.8043478260869565\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 111.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.774421163960439\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.8176949327585239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.4533715539666566\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8915984447896507\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.6844660194174758\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 423.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7258510378815234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.7130699923808707\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.4845773388188732\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9185101255585838\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9352798760436353\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9451817791436004\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7367268238041563\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.9243907804071344\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9464285714285714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9278919892277937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 9.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9645900387760589\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7875713260902645\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9439106178605402\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9124079759621928\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 151.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9328791569766661\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 22.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.961068431355243\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7804133302146596\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9474034175536131\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9187935034802784\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 396.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5144795179367065, 2.051693916320801)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.22870994075853848, 0.89307621413677)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.12643689229906357, 0.6561778378190428)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.45687170592512816, 0.9313252257205823)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.23620291529593407, 0.8486126665530496)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.023615064099431038, 0.038175035268068314)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0018870026106014848, 0.007062144577503204)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.020982682704925537, 0.034282296895980835)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.025089697912335396, 0.05523453280329704)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0030255536548793316, 0.022529417648911476)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03969914838671684, 0.10491261631250381)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0_to_fog_1.0_replay_2:8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/d3ded5c21585484ca6bee052f69311f0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0_to_fog_1.0_replay_2:8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.80 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "# ewc\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name fog_0_to_fog_1.0_replay_2:8 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82730bc3-510c-48c0-8619-6f246823604c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "#这个是使用ewc的增量训练在没有加雾的第一个数据集上的效果\n",
    "\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/testing/image_2/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b58b86b6-b29d-4683-b134-3b193425da83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_replay_2:8/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 cbe9b398 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198       0.93      0.827      0.893      0.644\n",
      "                   Car       2244       8711      0.953       0.91      0.965      0.783\n",
      "                   Van       2244        861      0.953      0.886      0.942      0.748\n",
      "                 Truck       2244        333      0.971       0.94      0.965        0.8\n",
      "                  Tram       2244        138      0.922      0.957      0.955      0.709\n",
      "            Pedestrian       2244       1286      0.899      0.729       0.82      0.466\n",
      "        Person_sitting       2244         89      0.904      0.531      0.689      0.395\n",
      "               Cyclist       2244        496      0.905      0.796       0.88      0.565\n",
      "                  Misc       2244        284      0.932      0.867      0.925      0.683\n",
      "Speed: 0.1ms pre-process, 0.9ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp83\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_replay_2:8/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d58221c4-fcd6-43d3-b00d-a4131f5b665c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# 然后是0.6雾测试集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/fogged_strength1.0/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78e8b479-bbe4-49d6-a930-b64b4d4e70de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_replay_2:8/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 cbe9b398 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.856      0.595      0.703      0.433\n",
      "                   Car       2244       8711      0.922      0.724       0.85      0.605\n",
      "                   Van       2244        861      0.871      0.639      0.747      0.514\n",
      "                 Truck       2244        333      0.874      0.568      0.689      0.425\n",
      "                  Tram       2244        138      0.784       0.63      0.731      0.402\n",
      "            Pedestrian       2244       1286      0.873      0.582      0.692      0.368\n",
      "        Person_sitting       2244         89      0.776      0.494      0.609      0.332\n",
      "               Cyclist       2244        496       0.88      0.486      0.594      0.345\n",
      "                  Misc       2244        284      0.871      0.637      0.712      0.472\n",
      "Speed: 0.0ms pre-process, 1.0ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp85\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_replay_2:8/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a782ac88-8bef-42bc-8429-2cd5563088ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc988979-143a-4123-85c8-7f7e378fedc7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62c599db-cfe4-46c8-8535-28f877617d83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f522573-67a8-4e83-8901-43e0b5fe9697",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开始回放。还是以1.0作为验证集\n",
    "from fog_test.for_different_strength import mix_dataset\n",
    "origin_ratio = {\n",
    "    '1.0':0.9,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )\n",
    "\n",
    "# val_fogged_strength = 1.0\n",
    "# # 替换验证集\n",
    "# update_testsets = f\" \\\n",
    "# rm ../datasets/kitti/images/val/* &&\\\n",
    "# cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "# echo 'Val set updated successfully!' \\\n",
    "# \" \n",
    "# !{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "938dbbc6-88f7-41a2-b5aa-2d8f02bad351",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0_to_fog_1.0_replay_1:9, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 155c1042 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/44fbd1eaded846b79dad64de570b9f4c\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0_to_fog_1.0_replay_1:9/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0_to_fog_1.0_replay_1:9\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.65G    0.03496     0.0344   0.007083        128        640: 1\n",
      "tensor([0.91561], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.656      0.335      0.364      0.207\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.65G    0.03315     0.0304   0.005579        197        640:  \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda information\n",
      "       1/99      3.65G    0.03378    0.03047   0.005208        133        640: 1\n",
      "tensor([0.94210], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.612      0.188      0.217      0.127\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.65G    0.03597    0.03245   0.006107        131        640: 1\n",
      "tensor([1.02088], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.595      0.302      0.326      0.177\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.65G    0.03814    0.03453   0.007028        108        640: 1\n",
      "tensor([0.92667], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.576       0.38       0.38      0.206\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.65G    0.03818    0.03304   0.006205        156        640: 1\n",
      "tensor([0.98522], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.781      0.618      0.696      0.399\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.65G    0.03665    0.03227   0.005719        123        640: 1\n",
      "tensor([0.94917], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.83      0.621      0.713      0.402\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.65G    0.03623    0.03147   0.005205        174        640: 1\n",
      "tensor([1.05652], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.667      0.769      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.65G    0.03593    0.03061   0.005094        166        640: 1\n",
      "tensor([1.05286], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.84      0.696       0.77      0.453\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.65G    0.03507    0.03061   0.004703        152        640: 1\n",
      "tensor([0.92848], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.688      0.782      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.65G    0.03485     0.0304   0.004666        136        640: 1\n",
      "tensor([0.88659], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.804      0.641      0.728      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.65G     0.0343    0.03016   0.004514        134        640: 1\n",
      "tensor([0.86588], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.688      0.795      0.492\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.65G    0.03406    0.02959   0.004352        182        640: 1\n",
      "tensor([0.94722], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.747      0.813      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.65G    0.03403    0.02983   0.004267        128        640: 1\n",
      "tensor([0.80459], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.726      0.808      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.65G    0.03332    0.02902    0.00412        112        640: 1\n",
      "tensor([0.90478], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.735      0.568      0.633      0.372\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.65G    0.03314    0.02927   0.004112        151        640: 1\n",
      "tensor([0.84244], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.796      0.587      0.662      0.402\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.65G    0.03314    0.02918   0.003957        132        640: 1\n",
      "tensor([0.84984], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.664      0.781      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.65G    0.03288     0.0287    0.00388        131        640: 1\n",
      "tensor([0.78222], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.728      0.831      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.65G    0.03269    0.02859   0.003892        159        640: 1\n",
      "tensor([0.95743], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.758      0.839      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.65G     0.0323     0.0284   0.003737        125        640: 1\n",
      "tensor([0.73007], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.724      0.819      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.65G    0.03235    0.02867   0.003776         88        640: 1\n",
      "tensor([0.68174], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871       0.75      0.828      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.65G    0.03213    0.02779    0.00378        137        640: 1\n",
      "tensor([0.91043], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867       0.77      0.837      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.65G    0.03151    0.02818   0.003591        166        640: 1\n",
      "tensor([0.86989], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.769      0.851      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.65G    0.03139    0.02782   0.003531        161        640: 1\n",
      "tensor([0.87782], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.751      0.848       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.65G    0.03149    0.02746   0.003483        118        640: 1\n",
      "tensor([0.78173], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891       0.78      0.853      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.65G    0.03137    0.02749   0.003473        151        640: 1\n",
      "tensor([0.86698], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.793      0.858      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.65G    0.03124    0.02765   0.003463        133        640: 1\n",
      "tensor([0.78816], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.787      0.853      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.65G    0.03069    0.02717   0.003316        154        640: 1\n",
      "tensor([0.94780], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.788      0.854       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.65G    0.03085    0.02746   0.003511        122        640: 1\n",
      "tensor([0.71956], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.852      0.767      0.822      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.65G    0.03077    0.02729   0.003218        123        640: 1\n",
      "tensor([0.65461], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.795      0.868      0.563\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.65G    0.03062    0.02685   0.003184        127        640: 1\n",
      "tensor([0.68986], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.783      0.865      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.65G    0.03021    0.02598   0.003223        127        640: 1\n",
      "tensor([0.71385], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.775      0.849      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.65G    0.03039     0.0266   0.003245        122        640: 1\n",
      "tensor([0.79597], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.776      0.857      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.65G    0.03012     0.0268   0.003078        146        640: 1\n",
      "tensor([0.82201], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.94      0.784      0.867      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.65G    0.02986     0.0262   0.003191        202        640: 1\n",
      "tensor([0.94588], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.728      0.826      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.65G    0.02988    0.02609   0.003204         94        640: 1\n",
      "tensor([0.62622], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.792      0.869       0.58\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.65G    0.02982    0.02618   0.003053        152        640: 1\n",
      "tensor([0.85468], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.796      0.869      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.65G    0.02935    0.02589   0.003107        123        640: 1\n",
      "tensor([0.69447], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.766      0.861      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.65G    0.02949    0.02608   0.003121        162        640: 1\n",
      "tensor([0.75145], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.779      0.858      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.65G    0.02948    0.02619   0.003014        161        640: 1\n",
      "tensor([0.77693], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863      0.814      0.864      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.65G    0.02934    0.02603   0.002968        122        640: 1\n",
      "tensor([0.69029], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.814      0.874      0.593\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.65G    0.02908     0.0256   0.003059        126        640: 1\n",
      "tensor([0.65462], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.786      0.871      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.65G    0.02879    0.02565   0.002991         90        640: 1\n",
      "tensor([0.60342], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.812       0.87      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.65G    0.02872    0.02544    0.00289        118        640: 1\n",
      "tensor([0.74181], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.804      0.875      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.65G    0.02872    0.02556   0.002754        157        640: 1\n",
      "tensor([0.81142], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.794      0.872      0.594\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.65G    0.02875    0.02553   0.002832        104        640: 1\n",
      "tensor([0.55087], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.804      0.871      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.65G    0.02856    0.02578   0.002789        157        640: 1\n",
      "tensor([0.73375], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892       0.83      0.875      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.65G    0.02858    0.02516   0.002667        108        640: 1\n",
      "tensor([0.57278], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.801      0.879      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.65G    0.02839    0.02504   0.002762        159        640: 1\n",
      "tensor([0.75607], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.806      0.874      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.65G    0.02803    0.02499   0.002709        118        640: 1\n",
      "tensor([0.68579], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.776      0.864      0.585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.65G    0.02824    0.02538   0.002737        176        640: 1\n",
      "tensor([0.88413], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.792      0.875      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.65G    0.02813    0.02494   0.002753        130        640: 1\n",
      "tensor([0.66977], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.823      0.864      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.65G    0.02778    0.02505   0.002608        178        640: 1\n",
      "tensor([0.87260], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.798       0.87      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.65G    0.02759    0.02458   0.002623        148        640: 1\n",
      "tensor([0.70487], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.795      0.878      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.65G    0.02754    0.02446   0.002535        115        640: 1\n",
      "tensor([0.65293], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.933        0.8       0.88      0.605\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.65G    0.02756    0.02438   0.002597        124        640: 1\n",
      "tensor([0.65925], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.806      0.871      0.603\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.65G    0.02718    0.02406   0.002646        163        640: 1\n",
      "tensor([0.70471], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.805       0.88      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.65G    0.02731    0.02446    0.00257        200        640: 1\n",
      "tensor([0.80327], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.833      0.887      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.65G    0.02726    0.02445   0.002571        141        640: 1\n",
      "tensor([0.68036], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.827      0.888      0.609\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.65G    0.02726     0.0244   0.002588        146        640: 1\n",
      "tensor([0.68607], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.818      0.885      0.613\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.65G    0.02697    0.02418   0.002574        168        640: 1\n",
      "tensor([0.70136], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.845      0.889       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.65G    0.02697    0.02393   0.002466        175        640: 1\n",
      "tensor([0.77990], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919        0.8       0.88      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.65G    0.02661    0.02403   0.002528        139        640: 1\n",
      "tensor([0.75151], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.803      0.884       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.65G    0.02652    0.02323   0.002441        117        640: 1\n",
      "tensor([0.62669], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.811       0.88      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.65G    0.02674    0.02359   0.002357        129        640: 1\n",
      "tensor([0.66331], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93       0.81      0.881      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.65G    0.02646     0.0237   0.002497        109        640: 1\n",
      "tensor([0.61057], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.827      0.882      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.65G    0.02642    0.02376   0.002343        154        640: 1\n",
      "tensor([0.75693], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.835      0.887      0.621\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.65G    0.02617    0.02355   0.002414        119        640: 1\n",
      "tensor([0.63439], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.823       0.88      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.65G    0.02614    0.02299   0.002392        153        640: 1\n",
      "tensor([0.69919], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.833       0.89      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.65G    0.02608    0.02345   0.002406        116        640: 1\n",
      "tensor([0.58477], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918       0.83      0.889      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.65G     0.0257    0.02288   0.002384        141        640: 1\n",
      "tensor([0.70228], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.818      0.886      0.625\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.65G    0.02585    0.02308   0.002318        175        640: 1\n",
      "tensor([0.81396], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.831       0.89       0.63\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.65G    0.02593    0.02318   0.002342        161        640: 1\n",
      "tensor([0.73155], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.825      0.885      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.65G    0.02566    0.02267   0.002203        114        640: 1\n",
      "tensor([0.61226], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.825      0.888       0.63\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.65G    0.02582    0.02322   0.002309        141        640: 1\n",
      "tensor([0.70222], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.806      0.885      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.65G    0.02565    0.02309   0.002226        133        640: 1\n",
      "tensor([0.60284], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.828       0.89      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.65G    0.02537    0.02257   0.002237        159        640: 1\n",
      "tensor([0.74472], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.835      0.889      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.65G    0.02555    0.02262   0.002257        122        640: 1\n",
      "tensor([0.56456], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.831      0.888      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.65G    0.02546    0.02281   0.002209        137        640: 1\n",
      "tensor([0.65297], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.822      0.883      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.65G    0.02537    0.02238   0.002211        137        640: 1\n",
      "tensor([0.65945], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.823      0.891      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.65G    0.02486      0.022   0.002229        161        640: 1\n",
      "tensor([0.71889], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.811      0.882      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.65G    0.02507    0.02245    0.00214        154        640: 1\n",
      "tensor([0.60714], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.808      0.891       0.64\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.65G    0.02489    0.02211   0.002149        181        640: 1\n",
      "tensor([0.74383], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.823      0.889      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.65G    0.02477    0.02205   0.002146        149        640: 1\n",
      "tensor([0.62137], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904       0.85      0.894      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.65G    0.02472    0.02208   0.002071        118        640: 1\n",
      "tensor([0.61333], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.842      0.895      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.65G     0.0246    0.02193   0.002098        178        640: 1\n",
      "tensor([0.75116], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.836      0.889      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.65G    0.02452    0.02192   0.002069        140        640: 1\n",
      "tensor([0.65918], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923       0.83      0.889      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.65G    0.02456    0.02195   0.002123        119        640: 1\n",
      "tensor([0.53645], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921       0.84      0.893      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.65G    0.02445    0.02203   0.002054        114        640: 1\n",
      "tensor([0.50694], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.827      0.891      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.65G    0.02457    0.02178   0.002134        117        640: 1\n",
      "tensor([0.54917], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.844      0.892      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.65G    0.02438    0.02184   0.002058        118        640: 1\n",
      "tensor([0.55030], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.833      0.893      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.65G    0.02423    0.02151   0.001969        115        640: 1\n",
      "tensor([0.57312], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.831       0.89      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.65G    0.02405    0.02127   0.002012        159        640: 1\n",
      "tensor([0.71780], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.831      0.891      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.65G    0.02405    0.02151    0.00205        165        640: 1\n",
      "tensor([0.68865], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.824      0.891      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.65G    0.02398    0.02119   0.001996        126        640: 1\n",
      "tensor([0.57870], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.843      0.892      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.65G    0.02388    0.02124   0.002026        112        640: 1\n",
      "tensor([0.57892], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.845      0.891      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.65G    0.02368    0.02108   0.001909        121        640: 1\n",
      "tensor([0.57346], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.837      0.888      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.65G    0.02391    0.02102   0.002026        195        640: 1\n",
      "tensor([0.64535], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905       0.84       0.89      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.65G     0.0239    0.02117    0.00196        101        640: 1\n",
      "tensor([0.56544], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.843       0.89      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.65G    0.02362    0.02102   0.001986        137        640: 1\n",
      "tensor([0.55871], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.839      0.889      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.65G    0.02367    0.02112   0.001925        115        640: 1\n",
      "tensor([0.49840], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916       0.84       0.89      0.649\n",
      "\n",
      "100 epochs completed in 0.986 hours.\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_replay_1:9/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_replay_1:9/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0_to_fog_1.0_replay_1:9/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916       0.84       0.89      0.648\n",
      "                   Car       1048       4012      0.944      0.917      0.966       0.79\n",
      "                   Van       1048        431      0.935      0.942      0.965      0.787\n",
      "                 Truck       1048        166      0.957      0.931      0.964      0.782\n",
      "                  Tram       1048         56      0.964      0.963      0.956      0.712\n",
      "            Pedestrian       1048        618      0.855      0.735      0.823      0.465\n",
      "        Person_sitting       1048         20      0.916        0.6      0.686       0.44\n",
      "               Cyclist       1048        234      0.881      0.823      0.876      0.581\n",
      "                  Misc       1048        138      0.878      0.812      0.884      0.631\n",
      "Results saved to \u001b[1mruns/train/fog_0_to_fog_1.0_replay_1:9\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0_to_fog_1.0_replay_1:9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/44fbd1eaded846b79dad64de570b9f4c\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9299690149211146\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 219.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9659169695997261\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.790380543180588\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9438392910496092\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9165004985044866\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3677.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8508509438369329\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 26.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8761713972464914\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5814420940557002\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8810110679669362\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.8226874436024763\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 193.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8437037414883007\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 15.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8844959639018221\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.6307331408772046\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8784586752106437\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.8115942028985508\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 112.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7901434597175778\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 77.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.822530254373155\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.4648620555863525\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8547355994823534\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7346278317152104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 454.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7249456970142568\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.686303249167119\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.43980848660471955\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9156162052943407\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9637547412205708\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 2.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9564922211188347\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7117084153135147\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.964248470749754\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9632615170468036\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 54.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9439006233337605\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 7.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9635496816289292\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7819782508468023\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9566883367901999\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9314502583149691\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 155.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9383591374628842\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 28.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9649649023283535\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7867029226593456\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9347508799121702\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9419953596287703\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 406.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5251196026802063, 2.221331834793091)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.21701012870504793, 0.8951326915043902)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.12723788153454016, 0.6488292158778319)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.5761524537438458, 0.9404265243489262)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.18848146031766164, 0.8504954167179497)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.023620257154107094, 0.038183245807886124)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0019087123218923807, 0.007083064876496792)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.021017787978053093, 0.03452826291322708)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.02499525062739849, 0.054472606629133224)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0029157951939851046, 0.024611176922917366)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03964678570628166, 0.11626538634300232)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0_to_fog_1.0_replay_1:9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/44fbd1eaded846b79dad64de570b9f4c\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0_to_fog_1.0_replay_1:9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.80 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "# ewc\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name fog_0_to_fog_1.0_replay_1:9 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6e36087-89c0-4e44-a46c-86388b277928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_replay_1:9/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 155c1042 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.908      0.833      0.894      0.633\n",
      "                   Car       2244       8711      0.946      0.912      0.963      0.777\n",
      "                   Van       2244        861      0.928        0.9      0.939      0.739\n",
      "                 Truck       2244        333      0.962       0.94      0.963      0.782\n",
      "                  Tram       2244        138      0.941      0.928      0.954      0.675\n",
      "            Pedestrian       2244       1286       0.88      0.732      0.815       0.46\n",
      "        Person_sitting       2244         89      0.796      0.618      0.746      0.415\n",
      "               Cyclist       2244        496      0.895      0.784      0.857      0.549\n",
      "                  Misc       2244        284      0.913      0.849      0.917      0.668\n",
      "Speed: 0.0ms pre-process, 0.7ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp86\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_replay_1:9/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d58c8df-a59a-4a73-9ade-e36cbc830fb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73fb6b10-8718-4c8f-9420-5e86dcbee6f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9b0d2ee-ec27-440b-a167-2f59e1b4fe34",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4836e885-a5e1-4849-8491-6b2b3b0989dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "454c45d1-3ff3-4f1c-80ba-ffc823858bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开始回放。还是以1.0作为验证集\n",
    "from fog_test.for_different_strength import mix_dataset\n",
    "origin_ratio = {\n",
    "    '1.0':0.7,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )\n",
    "\n",
    "# val_fogged_strength = 1.0\n",
    "# # 替换验证集\n",
    "# update_testsets = f\" \\\n",
    "# rm ../datasets/kitti/images/val/* &&\\\n",
    "# cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "# echo 'Val set updated successfully!' \\\n",
    "# \" \n",
    "# !{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b742ec8-f950-4ff0-8e82-f0aeffcad13a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0_to_fog_1.0_replay_3:7, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 155c1042 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/8fc0402164114c78995d70efe8319182\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0_to_fog_1.0_replay_3:7/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0_to_fog_1.0_replay_3:7\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.65G    0.03423    0.03332   0.006696        128        640: 1\n",
      "tensor([0.87504], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.642      0.394      0.436      0.248\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.65G    0.03301    0.02957   0.004706        133        640: 1\n",
      "tensor([0.90832], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.763      0.451      0.535      0.319\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.65G    0.03561    0.03193   0.005767        131        640: 1\n",
      "tensor([0.92153], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.519      0.199       0.21      0.109\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.65G    0.03759    0.03358    0.00664        108        640: 1\n",
      "tensor([0.88357], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.473      0.194      0.184      0.104\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.65G     0.0374     0.0326   0.005976        156        640: 1\n",
      "tensor([0.99272], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.786      0.561      0.648      0.371\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.65G    0.03673    0.03182   0.005448        123        640: 1\n",
      "tensor([0.87126], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.791      0.606      0.698      0.403\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.65G    0.03601    0.03111   0.005101        174        640: 1\n",
      "tensor([1.00613], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.796      0.683      0.766      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.65G    0.03537    0.03028   0.004965        166        640: 1\n",
      "tensor([1.08813], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.792      0.551      0.651      0.379\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.65G    0.03495    0.03053   0.004658        152        640: 1\n",
      "tensor([0.90875], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.825        0.7      0.782      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.65G    0.03458    0.03022    0.00463        136        640: 1\n",
      "tensor([0.88675], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.76      0.662      0.716      0.433\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.65G    0.03419    0.03015   0.004651        134        640: 1\n",
      "tensor([0.86709], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.832      0.653      0.732      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.65G    0.03413    0.02952   0.004435        182        640: 1\n",
      "tensor([0.91392], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.838      0.687      0.794      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.65G    0.03368    0.02966   0.004259        128        640: 1\n",
      "tensor([0.75643], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.82      0.701      0.779      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.65G    0.03335    0.02883   0.003968        112        640: 1\n",
      "tensor([0.84589], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.736      0.817      0.502\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.65G    0.03285    0.02906   0.004036        151        640: 1\n",
      "tensor([0.83907], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.743      0.823       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.65G    0.03294    0.02902   0.003874        132        640: 1\n",
      "tensor([0.83644], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.855      0.717      0.791       0.49\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.65G    0.03254    0.02851   0.003914        131        640: 1\n",
      "tensor([0.83630], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.733      0.821      0.514\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.65G    0.03283    0.02851   0.003843        159        640: 1\n",
      "tensor([0.92325], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.752      0.831      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.65G    0.03224    0.02816   0.003809        125        640: 1\n",
      "tensor([0.71881], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.741      0.822      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.65G    0.03223    0.02851    0.00379         88        640: 1\n",
      "tensor([0.65268], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.744      0.831      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.65G    0.03153     0.0275   0.003691        137        640: 1\n",
      "tensor([0.93192], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.735      0.833      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.65G    0.03165    0.02813   0.003573        166        640: 1\n",
      "tensor([0.94753], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.693      0.805      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.65G    0.03145    0.02773   0.003528        161        640: 1\n",
      "tensor([0.85784], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.762      0.843       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.65G    0.03133    0.02728   0.003503        118        640: 1\n",
      "tensor([0.78515], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.771      0.844      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.65G    0.03097    0.02738   0.003481        151        640: 1\n",
      "tensor([0.87163], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.859      0.713      0.801      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.65G    0.03124    0.02765   0.003491        133        640: 1\n",
      "tensor([0.79951], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.751      0.835      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.65G    0.03081    0.02722   0.003325        154        640: 1\n",
      "tensor([0.90157], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.775      0.849      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.65G    0.03086    0.02732   0.003458        122        640: 1\n",
      "tensor([0.75363], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871       0.73      0.818       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.65G    0.03061    0.02714   0.003247        123        640: 1\n",
      "tensor([0.68208], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906       0.78      0.858      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.65G    0.03037    0.02668   0.003218        127        640: 1\n",
      "tensor([0.68431], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.772      0.846      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.65G     0.0302    0.02591   0.003202        127        640: 1\n",
      "tensor([0.74079], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.772      0.851      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.65G    0.03001    0.02646   0.003201        122        640: 1\n",
      "tensor([0.78745], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.789      0.853      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.65G    0.03015     0.0267   0.003141        146        640: 1\n",
      "tensor([0.81531], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.764      0.868      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.65G    0.02984     0.0261   0.003048        202        640: 1\n",
      "tensor([0.92233], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.762       0.85      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.65G    0.02963    0.02599   0.003137         94        640: 1\n",
      "tensor([0.64481], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902       0.77      0.855      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.65G    0.02953    0.02599   0.003068        152        640: 1\n",
      "tensor([0.83647], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.805      0.861      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.65G    0.02895    0.02567    0.00305        123        640: 1\n",
      "tensor([0.69901], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.775      0.857       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.65G    0.02942    0.02599   0.003084        162        640: 1\n",
      "tensor([0.75251], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905       0.77       0.86      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.65G     0.0294    0.02617   0.003108        161        640: 1\n",
      "tensor([0.78029], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.797       0.87      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.65G    0.02925    0.02596   0.002983        122        640: 1\n",
      "tensor([0.68155], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.783      0.862      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.65G    0.02881    0.02546   0.003007        126        640: 1\n",
      "tensor([0.66133], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912       0.78      0.852       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.65G    0.02884    0.02563    0.00298         90        640: 1\n",
      "tensor([0.63642], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.782      0.871      0.588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.65G    0.02861    0.02538   0.002857        118        640: 1\n",
      "tensor([0.72470], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881       0.82      0.873      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.65G    0.02856    0.02539   0.002762        157        640: 1\n",
      "tensor([0.77476], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.788      0.856      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.65G    0.02867    0.02545   0.002782        104        640: 1\n",
      "tensor([0.55597], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.816      0.867      0.588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.65G    0.02854    0.02576   0.002726        157        640: 1\n",
      "tensor([0.74197], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.792      0.864      0.588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.65G    0.02834    0.02509   0.002672        108        640: 1\n",
      "tensor([0.57750], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.818      0.882      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.65G    0.02841    0.02509   0.002761        159        640: 1\n",
      "tensor([0.76063], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.801      0.871      0.585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.65G    0.02793    0.02492   0.002736        118        640: 1\n",
      "tensor([0.65192], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.782       0.86      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.65G    0.02822    0.02539   0.002758        176        640: 1\n",
      "tensor([0.86700], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.797      0.871      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.65G    0.02814    0.02492   0.002693        130        640: 1\n",
      "tensor([0.68863], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.833      0.884      0.603\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.65G    0.02764    0.02492   0.002639        178        640: 1\n",
      "tensor([0.86279], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.794      0.867      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.65G    0.02761     0.0246   0.002636        148        640: 1\n",
      "tensor([0.70013], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.806      0.875        0.6\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.65G    0.02744    0.02439   0.002573        115        640: 1\n",
      "tensor([0.67325], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.809      0.876      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.65G    0.02749    0.02431   0.002613        124        640: 1\n",
      "tensor([0.64962], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.795      0.877      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.65G    0.02718    0.02399    0.00257        163        640: 1\n",
      "tensor([0.72703], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.809      0.874      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.65G     0.0271    0.02434   0.002551        200        640: 1\n",
      "tensor([0.77986], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.818      0.878      0.609\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.65G    0.02715    0.02435   0.002528        141        640: 1\n",
      "tensor([0.68116], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927       0.81      0.885      0.613\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.65G    0.02716    0.02426   0.002578        146        640: 1\n",
      "tensor([0.67970], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.793      0.874      0.609\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.65G    0.02688    0.02407    0.00254        168        640: 1\n",
      "tensor([0.69443], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.796      0.867      0.606\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.65G    0.02691    0.02389   0.002463        175        640: 1\n",
      "tensor([0.76311], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.797      0.874      0.609\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.65G    0.02648    0.02393   0.002491        139        640: 1\n",
      "tensor([0.75242], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.805       0.88      0.617\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.65G    0.02645    0.02311   0.002403        117        640: 1\n",
      "tensor([0.62646], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.816      0.882      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.65G    0.02657    0.02351   0.002382        129        640: 1\n",
      "tensor([0.64911], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.789      0.869      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.65G    0.02636     0.0236   0.002483        109        640: 1\n",
      "tensor([0.60457], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.814      0.877      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.65G    0.02641     0.0237   0.002335        154        640: 1\n",
      "tensor([0.78256], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.815      0.878      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.65G    0.02611    0.02353   0.002338        119        640: 1\n",
      "tensor([0.64039], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918        0.8      0.884      0.621\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.65G    0.02607    0.02298   0.002381        153        640: 1\n",
      "tensor([0.69860], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.819      0.883       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.65G    0.02605    0.02346    0.00242        116        640: 1\n",
      "tensor([0.61301], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.812      0.876      0.617\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.65G     0.0257     0.0228   0.002404        141        640: 1\n",
      "tensor([0.73559], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.793      0.872      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.65G    0.02578    0.02303   0.002315        175        640: 1\n",
      "tensor([0.82048], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898       0.81      0.872      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.65G    0.02583    0.02304   0.002371        161        640: 1\n",
      "tensor([0.71699], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.814      0.882       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.65G    0.02561    0.02267   0.002201        114        640: 1\n",
      "tensor([0.63375], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.816       0.88      0.625\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.65G    0.02576    0.02315   0.002292        141        640: 1\n",
      "tensor([0.70814], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.813      0.878      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.65G    0.02561    0.02311   0.002235        133        640: 1\n",
      "tensor([0.58771], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.838      0.884      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.65G    0.02528    0.02253   0.002203        159        640: 1\n",
      "tensor([0.74725], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.933      0.803      0.881      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.65G     0.0255    0.02255   0.002254        122        640: 1\n",
      "tensor([0.56749], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.815      0.885      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.65G    0.02545    0.02285   0.002229        137        640: 1\n",
      "tensor([0.66180], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.819      0.887      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.65G    0.02533    0.02241   0.002204        137        640: 1\n",
      "tensor([0.65102], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.816      0.885      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.65G    0.02482    0.02201   0.002225        161        640: 1\n",
      "tensor([0.72782], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.836      0.886      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.65G      0.025    0.02242   0.002147        154        640: 1\n",
      "tensor([0.61478], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.823       0.89      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.65G    0.02485    0.02213   0.002174        181        640: 1\n",
      "tensor([0.74777], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.804      0.883      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.65G    0.02473    0.02203   0.002119        149        640: 1\n",
      "tensor([0.63991], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.824      0.885      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.65G    0.02468    0.02203   0.002055        118        640: 1\n",
      "tensor([0.61074], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.853      0.893       0.64\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.65G    0.02456    0.02188   0.002101        178        640: 1\n",
      "tensor([0.75264], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.834      0.882      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.65G    0.02449    0.02185   0.002045        140        640: 1\n",
      "tensor([0.64401], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.837      0.885      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.65G    0.02452    0.02196   0.002127        119        640: 1\n",
      "tensor([0.52395], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924       0.82      0.886       0.64\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.65G    0.02442    0.02199   0.002035        114        640: 1\n",
      "tensor([0.49980], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.818      0.884      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.65G    0.02449     0.0217   0.002121        117        640: 1\n",
      "tensor([0.55123], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.817      0.889      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.65G    0.02435     0.0218   0.002043        118        640: 1\n",
      "tensor([0.55132], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.836      0.889      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.65G    0.02423    0.02143   0.001958        115        640: 1\n",
      "tensor([0.58643], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.817      0.884      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.65G    0.02399    0.02128   0.001998        159        640: 1\n",
      "tensor([0.70291], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.818      0.887      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.65G    0.02404    0.02155   0.002036        165        640: 1\n",
      "tensor([0.67847], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.814      0.883      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.65G    0.02389    0.02116   0.002008        126        640: 1\n",
      "tensor([0.57495], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.819      0.885      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.65G    0.02383    0.02119    0.00203        112        640: 1\n",
      "tensor([0.55784], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.839      0.887      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.65G    0.02366    0.02102   0.001871        121        640: 1\n",
      "tensor([0.56310], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.832      0.886      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.65G    0.02386    0.02097   0.002024        195        640: 1\n",
      "tensor([0.62378], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.821      0.885      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.65G    0.02384    0.02106   0.001953        101        640: 1\n",
      "tensor([0.59656], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916       0.82      0.885      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.65G    0.02358    0.02092   0.001988        137        640: 1\n",
      "tensor([0.54235], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.819      0.885      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.65G    0.02362    0.02108   0.001933        115        640: 1\n",
      "tensor([0.49407], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.815      0.886       0.65\n",
      "\n",
      "100 epochs completed in 0.995 hours.\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_replay_3:7/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_replay_3:7/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0_to_fog_1.0_replay_3:7/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.815      0.885       0.65\n",
      "                   Car       1048       4012       0.95      0.906      0.964      0.785\n",
      "                   Van       1048        431       0.93      0.907      0.957      0.777\n",
      "                 Truck       1048        166      0.951      0.926      0.953      0.775\n",
      "                  Tram       1048         56      0.915      0.911       0.95      0.725\n",
      "            Pedestrian       1048        618      0.875      0.691        0.8       0.45\n",
      "        Person_sitting       1048         20      0.905        0.6      0.703      0.458\n",
      "               Cyclist       1048        234      0.908      0.758      0.866      0.592\n",
      "                  Misc       1048        138      0.917      0.819      0.891      0.636\n",
      "Results saved to \u001b[1mruns/train/fog_0_to_fog_1.0_replay_3:7\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0_to_fog_1.0_replay_3:7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/8fc0402164114c78995d70efe8319182\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9273340832735394\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 193.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9636192670381449\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7846956935999205\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9495857001565604\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9061014321067494\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3635.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.826393630554246\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 18.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8657184873761804\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5916038635576539\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.9079021721878865\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.7583145823886565\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 177.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8653164626938996\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8912504540942274\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.6359852369048218\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.9173855782743142\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.8188405797101449\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 113.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7722587134625518\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 61.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.7997261154583413\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.4500792050154837\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8750246009959711\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.6910942380198043\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 427.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7215415587455306\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.7030199666470776\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.4577228037074841\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9048328923035266\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9130228695153328\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 5.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9501017305020106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7252961009530978\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.9153431871906945\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9107142857142857\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9380578778162818\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 8.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9530928336111316\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7748144559301101\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9505257372496593\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9259128606518164\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 154.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9186788375806602\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 29.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9566905826912979\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7774155280418185\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9304596923394759\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9071925754060325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 391.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.53166264295578, 2.258500099182129)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.18421373637867156, 0.8932674332477153)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.10368400747331909, 0.6496420904913801)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.472698929928589, 0.9331557660928995)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.19367381654471416, 0.8531224571827867)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.023579144850373268, 0.03759053722023964)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0018714829348027706, 0.006696032360196114)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.02092193253338337, 0.033580757677555084)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.025271710008382797, 0.06064171344041824)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0031111515127122402, 0.02700921520590782)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.039901040494441986, 0.09625567495822906)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0_to_fog_1.0_replay_3:7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/8fc0402164114c78995d70efe8319182\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0_to_fog_1.0_replay_3:7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.81 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 44.54 KB/260.54 KB\n"
     ]
    }
   ],
   "source": [
    "# ewc\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name fog_0_to_fog_1.0_replay_3:7 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "242e2c0e-04b8-45ca-8274-d60720fae30a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_replay_3:7/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 155c1042 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.913      0.831      0.904       0.65\n",
      "                   Car       2244       8711      0.957      0.908      0.966      0.791\n",
      "                   Van       2244        861      0.942      0.902      0.939      0.749\n",
      "                 Truck       2244        333      0.972      0.949      0.964        0.8\n",
      "                  Tram       2244        138      0.908       0.92      0.955      0.702\n",
      "            Pedestrian       2244       1286      0.884      0.725       0.82      0.469\n",
      "        Person_sitting       2244         89      0.801      0.607      0.782      0.436\n",
      "               Cyclist       2244        496        0.9      0.792      0.882      0.568\n",
      "                  Misc       2244        284      0.937      0.843      0.921      0.687\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp87\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_replay_3:7/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9637657-5bd4-4e3b-aa69-50190b924d91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a918cf6-521b-45e5-9f6a-2c13b516b45a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db5c6cc9-c2cf-4e32-a3c2-506d10509d22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "028b39dc-a991-490e-918c-51c94cd8d42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开始回放。还是以1.0作为验证集\n",
    "from fog_test.for_different_strength import mix_dataset\n",
    "origin_ratio = {\n",
    "    '1.0':0.6,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )\n",
    "\n",
    "# val_fogged_strength = 1.0\n",
    "# # 替换验证集\n",
    "# update_testsets = f\" \\\n",
    "# rm ../datasets/kitti/images/val/* &&\\\n",
    "# cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "# echo 'Val set updated successfully!' \\\n",
    "# \" \n",
    "# !{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6be6d4ff-8725-427a-a5ce-e5750d935d6a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0_to_fog_1.0_replay_4:6, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 155c1042 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/bfbeacdfee3544cabb2294f16177cbae\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0_to_fog_1.0_replay_4:6/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0_to_fog_1.0_replay_4:6\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.65G    0.04002      0.047    0.01191        183        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/99      3.65G    0.03362    0.03265   0.006539        128        640: 1\n",
      "tensor([0.87118], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.787      0.476      0.553      0.324\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.65G    0.03279    0.02903    0.00462        133        640: 1\n",
      "tensor([0.95578], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.566      0.301      0.316      0.182\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.65G    0.03539    0.03124   0.005389        131        640: 1\n",
      "tensor([0.95015], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.746      0.425      0.501       0.26\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.65G    0.03693    0.03301   0.006233        108        640: 1\n",
      "tensor([0.83610], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.522      0.129      0.137     0.0811\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.65G    0.03719    0.03258   0.006061        156        640: 1\n",
      "tensor([0.97517], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.783      0.547      0.649      0.374\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.65G    0.03651     0.0316   0.005401        123        640: 1\n",
      "tensor([0.95080], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.75      0.442      0.525      0.303\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.65G    0.03558    0.03097   0.005159        174        640: 1\n",
      "tensor([0.99701], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.621      0.729       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.65G    0.03537    0.03026   0.004929        166        640: 1\n",
      "tensor([1.01328], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.784      0.672      0.743      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.65G    0.03477    0.03029   0.004532        152        640: 1\n",
      "tensor([0.87331], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.837      0.684      0.779      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.65G    0.03427    0.03001   0.004651        136        640: 1\n",
      "tensor([0.87242], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.847      0.704      0.791       0.49\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.65G    0.03424    0.02983   0.004333        134        640: 1\n",
      "tensor([0.84269], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858       0.68      0.773      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.65G    0.03353    0.02924   0.004173        182        640: 1\n",
      "tensor([0.92701], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.696      0.791      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.65G    0.03364    0.02948   0.004125        128        640: 1\n",
      "tensor([0.73849], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887       0.71      0.806      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.65G    0.03305    0.02875   0.004016        112        640: 1\n",
      "tensor([0.85368], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.707      0.785      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.65G    0.03294    0.02893   0.004048        151        640: 1\n",
      "tensor([0.84223], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.702      0.806      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.65G    0.03271    0.02886    0.00393        132        640: 1\n",
      "tensor([0.82575], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.721      0.825       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.65G    0.03229    0.02846   0.003893        131        640: 1\n",
      "tensor([0.81149], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.854      0.717      0.805      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.65G    0.03257    0.02831   0.003754        159        640: 1\n",
      "tensor([0.93223], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.725      0.807      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.65G    0.03215    0.02805   0.003796        125        640: 1\n",
      "tensor([0.72780], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.803      0.756      0.809      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.65G    0.03216    0.02831   0.003708         88        640: 1\n",
      "tensor([0.68947], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.733      0.827      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.65G    0.03165    0.02755   0.003739        137        640: 1\n",
      "tensor([0.89898], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.764      0.845       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.65G    0.03145    0.02801   0.003522        166        640: 1\n",
      "tensor([0.90168], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.732      0.827      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.65G    0.03126    0.02764   0.003477        161        640: 1\n",
      "tensor([0.90833], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.754      0.842      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.65G    0.03124    0.02722     0.0035        118        640: 1\n",
      "tensor([0.74220], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.771      0.838      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.65G    0.03088    0.02718   0.003401        151        640: 1\n",
      "tensor([0.88063], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.705      0.817      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.65G    0.03087    0.02745   0.003488        133        640: 1\n",
      "tensor([0.77678], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.784      0.848      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.65G    0.03083    0.02723   0.003325        154        640: 1\n",
      "tensor([0.88150], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88       0.75      0.831      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.65G    0.03063    0.02714   0.003357        122        640: 1\n",
      "tensor([0.74303], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.755      0.838      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.65G    0.03041      0.027   0.003245        123        640: 1\n",
      "tensor([0.63116], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.778      0.851      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.65G    0.03022    0.02661   0.003229        127        640: 1\n",
      "tensor([0.64977], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.731      0.823      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.65G    0.02985    0.02573   0.003171        127        640: 1\n",
      "tensor([0.70971], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.775      0.855      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.65G    0.03002    0.02644   0.003192        122        640: 1\n",
      "tensor([0.74465], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.748      0.839      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.65G    0.03014    0.02664   0.003106        146        640: 1\n",
      "tensor([0.82287], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.753      0.841      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.65G    0.02965    0.02593   0.003065        202        640: 1\n",
      "tensor([0.89388], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.859      0.766      0.822      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.65G    0.02957    0.02589   0.003087         94        640: 1\n",
      "tensor([0.60524], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.794      0.853      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.65G    0.02944    0.02589    0.00302        152        640: 1\n",
      "tensor([0.84811], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.761      0.845      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.65G    0.02931     0.0257   0.003057        123        640: 1\n",
      "tensor([0.68931], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897       0.79      0.858      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.65G    0.02921    0.02591   0.003109        162        640: 1\n",
      "tensor([0.75614], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.759      0.851      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.65G    0.02917    0.02605   0.003017        161        640: 1\n",
      "tensor([0.76867], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.776       0.86      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.65G    0.02898    0.02583   0.002903        122        640: 1\n",
      "tensor([0.67264], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.797      0.873      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.65G    0.02901    0.02538   0.002958        126        640: 1\n",
      "tensor([0.65861], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.784      0.868      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.65G    0.02872    0.02554   0.003012         90        640: 1\n",
      "tensor([0.63123], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.765      0.863      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.65G    0.02847    0.02531   0.002816        118        640: 1\n",
      "tensor([0.69194], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909        0.8      0.867      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.65G    0.02862    0.02531   0.002712        157        640: 1\n",
      "tensor([0.77345], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.767      0.869      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.65G    0.02822    0.02524   0.002758        104        640: 1\n",
      "tensor([0.58143], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.791      0.863       0.59\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.65G    0.02853    0.02552   0.002638        157        640: 1\n",
      "tensor([0.74879], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.793      0.872      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.65G    0.02827    0.02477   0.002649        108        640: 1\n",
      "tensor([0.54835], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.786      0.864      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.65G    0.02819    0.02498   0.002784        159        640: 1\n",
      "tensor([0.72555], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.791      0.865      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.65G    0.02783    0.02483   0.002691        118        640: 1\n",
      "tensor([0.67825], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.774      0.854      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.65G    0.02799    0.02521   0.002746        176        640: 1\n",
      "tensor([0.85841], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.778      0.863      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.65G    0.02793    0.02475   0.002683        130        640: 1\n",
      "tensor([0.69648], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.795       0.87      0.593\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.65G    0.02759    0.02481   0.002603        178        640: 1\n",
      "tensor([0.85532], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.789      0.867      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.65G    0.02752    0.02451   0.002631        148        640: 1\n",
      "tensor([0.68857], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.796      0.871      0.603\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.65G    0.02732    0.02433   0.002557        115        640: 1\n",
      "tensor([0.65852], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.814      0.875      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.65G    0.02741    0.02423   0.002578        124        640: 1\n",
      "tensor([0.62376], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.786       0.87      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.65G    0.02703    0.02386   0.002549        163        640: 1\n",
      "tensor([0.70020], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.795      0.875       0.61\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.65G    0.02705    0.02425   0.002506        200        640: 1\n",
      "tensor([0.78932], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.812      0.875      0.608\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.65G    0.02698    0.02417   0.002481        141        640: 1\n",
      "tensor([0.66410], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.817      0.884      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.65G    0.02713    0.02423    0.00256        146        640: 1\n",
      "tensor([0.67720], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.831       0.88      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.65G    0.02678    0.02403   0.002496        168        640: 1\n",
      "tensor([0.68059], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.812      0.874       0.61\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.65G    0.02675    0.02371   0.002441        175        640: 1\n",
      "tensor([0.73861], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.809      0.874      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.65G    0.02645    0.02383    0.00243        139        640: 1\n",
      "tensor([0.75570], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906       0.81       0.88      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.65G    0.02631    0.02304   0.002448        117        640: 1\n",
      "tensor([0.62795], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.801      0.877      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.65G    0.02655    0.02347   0.002377        129        640: 1\n",
      "tensor([0.65641], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.796      0.877      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.65G    0.02637    0.02357   0.002467        109        640: 1\n",
      "tensor([0.61908], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928       0.81      0.878      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.65G    0.02634    0.02369   0.002388        154        640: 1\n",
      "tensor([0.77556], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.824      0.879      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.65G    0.02594    0.02331   0.002376        119        640: 1\n",
      "tensor([0.60940], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.819      0.884      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.65G    0.02597    0.02289   0.002422        153        640: 1\n",
      "tensor([0.72439], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.804      0.875      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.65G    0.02594    0.02337    0.00239        116        640: 1\n",
      "tensor([0.63870], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.806      0.873      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.65G    0.02557    0.02267   0.002356        141        640: 1\n",
      "tensor([0.70682], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.823      0.881      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.65G    0.02568    0.02294   0.002285        175        640: 1\n",
      "tensor([0.79344], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.812      0.878      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.65G    0.02581    0.02302   0.002339        161        640: 1\n",
      "tensor([0.70921], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.821      0.882      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.65G    0.02554    0.02258    0.00219        114        640: 1\n",
      "tensor([0.60676], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.826      0.882      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.65G    0.02574    0.02308   0.002353        141        640: 1\n",
      "tensor([0.70474], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.806      0.877      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.65G    0.02553      0.023   0.002238        133        640: 1\n",
      "tensor([0.59055], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917       0.82      0.879      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.65G    0.02525    0.02251   0.002191        159        640: 1\n",
      "tensor([0.75024], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.815      0.878      0.632\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.65G    0.02547    0.02254   0.002252        122        640: 1\n",
      "tensor([0.55923], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.812      0.883      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.65G    0.02538    0.02274   0.002216        137        640: 1\n",
      "tensor([0.65918], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.816      0.881      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.65G    0.02524    0.02223   0.002202        137        640: 1\n",
      "tensor([0.64943], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.819      0.882      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.65G     0.0247     0.0218   0.002214        161        640: 1\n",
      "tensor([0.74750], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.819      0.883      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.65G    0.02501    0.02245   0.002111        154        640: 1\n",
      "tensor([0.61763], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.825      0.887      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.65G    0.02479    0.02201   0.002149        181        640: 1\n",
      "tensor([0.72366], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.822      0.883      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.65G    0.02471    0.02198    0.00218        149        640: 1\n",
      "tensor([0.63573], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.829      0.882      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.65G    0.02462    0.02205   0.002102        118        640: 1\n",
      "tensor([0.57819], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.942       0.81      0.886      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.65G    0.02444    0.02182   0.002063        178        640: 1\n",
      "tensor([0.74229], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.812      0.883      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.65G    0.02439     0.0218   0.002064        140        640: 1\n",
      "tensor([0.64935], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.829      0.881      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.65G    0.02448    0.02185   0.002123        119        640: 1\n",
      "tensor([0.52754], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.835      0.887      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.65G    0.02437      0.022   0.002056        114        640: 1\n",
      "tensor([0.50770], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.812      0.881      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.65G    0.02442    0.02165    0.00208        117        640: 1\n",
      "tensor([0.54969], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.819      0.883      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.65G    0.02426    0.02172   0.002031        118        640: 1\n",
      "tensor([0.52213], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.819      0.882      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.65G    0.02414    0.02143   0.001956        115        640: 1\n",
      "tensor([0.56632], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.839      0.883      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.65G    0.02396    0.02121   0.002008        159        640: 1\n",
      "tensor([0.71865], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.827      0.885      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.65G    0.02395    0.02139   0.002032        165        640: 1\n",
      "tensor([0.68430], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.823      0.884      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.65G    0.02385    0.02115   0.002022        126        640: 1\n",
      "tensor([0.57741], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.831      0.883      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.65G     0.0238    0.02112   0.002033        112        640: 1\n",
      "tensor([0.57825], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929       0.82      0.883      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.65G    0.02355    0.02096    0.00187        121        640: 1\n",
      "tensor([0.58828], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.937      0.819      0.884      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.65G    0.02386    0.02096   0.002005        195        640: 1\n",
      "tensor([0.63654], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.933      0.818      0.884      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.65G    0.02381      0.021   0.001931        101        640: 1\n",
      "tensor([0.55785], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.818      0.882      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.65G    0.02354     0.0209   0.002003        137        640: 1\n",
      "tensor([0.55071], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.831      0.883      0.651\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.65G    0.02359    0.02101   0.001943        115        640: 1\n",
      "tensor([0.48739], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.811      0.884      0.651\n",
      "\n",
      "100 epochs completed in 1.009 hours.\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_replay_4:6/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0_to_fog_1.0_replay_4:6/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0_to_fog_1.0_replay_4:6/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915       0.83      0.883      0.651\n",
      "                   Car       1048       4012      0.944      0.914      0.964      0.785\n",
      "                   Van       1048        431      0.947      0.912      0.954      0.776\n",
      "                 Truck       1048        166       0.95      0.915       0.95      0.778\n",
      "                  Tram       1048         56      0.939      0.946      0.956      0.725\n",
      "            Pedestrian       1048        618      0.874      0.709      0.806      0.451\n",
      "        Person_sitting       1048         20      0.927      0.638      0.709      0.488\n",
      "               Cyclist       1048        234      0.892      0.791      0.848      0.571\n",
      "                  Misc       1048        138       0.85       0.82      0.877      0.632\n",
      "Results saved to \u001b[1mruns/train/fog_0_to_fog_1.0_replay_4:6\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0_to_fog_1.0_replay_4:6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/bfbeacdfee3544cabb2294f16177cbae\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9288703221523473\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 216.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.964029838292505\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7845383591164995\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9443652981401842\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9138756153960541\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3666.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8380288321484588\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 23.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8483368559023147\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5711248345078402\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8915136091646403\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.7905982905982906\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 185.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8346110578486511\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 20.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8770480811757693\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.6320267760627302\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8497971407858065\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.819958204016175\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 113.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7827569436624984\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 63.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.8059384375056814\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.45109433097109103\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8742318763374153\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7086116253794248\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 438.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7557410186434742\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.7092091052159947\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.48827091252743415\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9272290938957606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6377847544514211\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9427286239093982\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9555563063819541\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7252812856057875\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.9390574927269029\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9464285714285714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9319833421493744\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 8.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9503605311343908\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.778356340675758\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9499489477187735\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.914684661118673\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 152.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9291256076565734\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 22.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9542193945998615\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7759164582704485\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9470868492433012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9118329466357309\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 393.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5122725963592529, 2.0500006675720215)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.13743505532348496, 0.8872915465734184)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.08109577481575289, 0.6511257960963118)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.5216887713849239, 0.9419071404926742)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.12936068578597915, 0.8387964138551218)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.02354387752711773, 0.03718707337975502)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0018703637178987265, 0.006538845133036375)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.020899489521980286, 0.033011358231306076)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.025316940620541573, 0.07615581899881363)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0029752131085842848, 0.05890410393476486)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.040205176919698715, 0.13070690631866455)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0_to_fog_1.0_replay_4:6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/bfbeacdfee3544cabb2294f16177cbae\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0_to_fog_1.0_replay_4:6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.79 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "# ewc\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name fog_0_to_fog_1.0_replay_4:6 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1f39208-cf18-436e-90c3-61feef403610",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_replay_4:6/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 155c1042 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.926      0.845      0.905      0.655\n",
      "                   Car       2244       8711      0.955      0.918      0.967      0.792\n",
      "                   Van       2244        861      0.952        0.9      0.949      0.763\n",
      "                 Truck       2244        333       0.98      0.949      0.974      0.814\n",
      "                  Tram       2244        138      0.921      0.957      0.959      0.717\n",
      "            Pedestrian       2244       1286      0.889      0.739      0.832      0.475\n",
      "        Person_sitting       2244         89      0.837      0.607       0.73      0.408\n",
      "               Cyclist       2244        496      0.927      0.819      0.898      0.579\n",
      "                  Misc       2244        284      0.947       0.87      0.932       0.69\n",
      "Speed: 0.0ms pre-process, 0.8ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp88\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_replay_4:6/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f84c57a1-6e74-41fd-9292-9cb287fcdf59",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
